## Archive for the ‘Text Mining’ Category

### Text Mining For Lawyers (The 55% Google Weaned Lawyers Are Missing)

Thursday, May 4th, 2017

Working the Mines: How Text Mining Can Help Create Value for Lawyers by Rees Morrison, Juris Datoris, Legaltech News.

From the post:

To most lawyers, text mining may sound like a magic wand or more hype regarding “artificial intelligence.” In fact, with the right input, text mining is a well-grounded genre of software that can find patterns and insights from large amounts of written material. So, if your law firm or law department has a sizable amount of text from various sources, it can extract value from that collection through powerful software tools.

To help lawyers recognize the potential of text mining and demystify it, this article digs through typical steps of a project. Terms of art related to this domain of software are in bold and, yes, there will be a quiz at the end.

Our example project assumes that your law firm (or law department) has gathered a raft of written comments through an internal survey of lawyers or from clients who have typed their views in a client satisfaction survey (perhaps in response to an open-ended question like “In what ways could we improve?”). All that writing is grist for the mill of text mining!

Great overview of the benefits and complexities of text mining!

I was recently assured by a Google weaned lawyer that natural language searching enabled him and his friends to do a few quick searches to find relevant authorities.

I could not help but point out my review of Blair and Maron’s work that demonstrated while attorneys estimated they recovered 75% of relevant documents, in fact they recovered barely 20%.

No solution returns 100% of the relevant documents for any non-trivial dataset, but leaving 55% on the floor doesn’t inspire confidence.

Especially when searchers consider a relevant result to be success. Depends.

Depends on how many relevant authorities existed and if any were closer to your facts than those found? Among other things.

Is a relevant result your test for research success or the best relevant research result, with a measure of confidence in it’s quality?

### “Tidying” Up Jane Austen (R)

Thursday, February 16th, 2017

Thanks to Julia’s presentation I now know there is an R package with all of Jane Austen’s novels ready for text analysis.

OK, Austen may not be at the top of your reading list, but the Tidy techniques Julia demonstrates are applicable to a wide range of textual data.

Among those mentioned in the presentation, NASA datasets!

Julia, along with Dave Robinson, wrote: Text Mining with R: A Tidy Approach, available online now and later this year from O’Reilly.

### Stanford CoreNLP – a suite of core NLP tools (3.7.0)

Thursday, January 12th, 2017

Stanford CoreNLP – a suite of core NLP tools

The beta is over and Stanford CoreNLP 3.7.0 is on the street!

From the webpage:

Stanford CoreNLP provides a set of natural language analysis tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract particular or open-class relations between entity mentions, get quotes people said, etc.

Choose Stanford CoreNLP if you need:

• An integrated toolkit with a good range of grammatical analysis tools
• Fast, reliable analysis of arbitrary texts
• The overall highest quality text analytics
• Support for a number of major (human) languages
• Available interfaces for most major modern programming languages
• Ability to run as a simple web service

Stanford CoreNLP’s goal is to make it very easy to apply a bunch of linguistic analysis tools to a piece of text. A tool pipeline can be run on a piece of plain text with just two lines of code. CoreNLP is designed to be highly flexible and extensible. With a single option you can change which tools should be enabled and which should be disabled. Stanford CoreNLP integrates many of Stanford’s NLP tools, including the part-of-speech (POS) tagger, the named entity recognizer (NER), the parser, the coreference resolution system, sentiment analysis, bootstrapped pattern learning, and the open information extraction tools. Moreover, an annotator pipeline can include additional custom or third-party annotators. CoreNLP’s analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications.

What stream of noise, sorry, news are you going to pipeling into the Stanford CoreNLP framework?

😉

Imagine a web service that offers levels of analysis alongside news text.

Or does the same with leaked emails and/or documents?

### How to get superior text processing in Python with Pynini

Saturday, November 19th, 2016

From the post:

It’s hard to beat regular expressions for basic string processing. But for many problems, including some deceptively simple ones, we can get better performance with finite-state transducers (or FSTs). FSTs are simply state machines which, as the name suggests, have a finite number of states. But before we talk about all the things you can do with FSTs, from fast text annotation—with none of the catastrophic worst-case behavior of regular expressions—to simple natural language generation, or even speech recognition, let’s explore what a state machine is, what they have to do with regular expressions.

Reporters, researchers and others will face a 2017 where the rate of information has increased, along with noise from media spasms over the latest taut from president-elect Trump.

Tagging text is the first example. Think about auto-generating graphs from emails with “to:,” “from:,” “date:,” and key terms in the email. Tagging the key terms is essential to that process.

Once tagged, you can slice and dice the text as more information is uncovered.

Interested?

### Stanford CoreNLP v3.7.0 beta is out! [Time is short, comments, bug reports, now!]

Thursday, November 3rd, 2016

Stanford CoreNLP v3.7.0 beta

The tweets I saw from Stanford NLP Group read:

Stanford CoreNLP v3.7.0 beta is out—improved coreference, dep parsing—KBP relation annotator—Arabic pipeline #NLProc

We‘re doing an official CoreNLP beta release this time, so bugs, comments, and fixes especially appreciated over the next two weeks!

OK, so, what are you waiting for? 😉

Oh, the standard blurb for your boss on why Stanford CoreNLP should be taking up your time:

Stanford CoreNLP provides a set of natural language analysis tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, and mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract open-class relations between mentions, etc.

Choose Stanford CoreNLP if you need:

• An integrated toolkit with a good range of grammatical analysis tools
• Fast, reliable analysis of arbitrary texts
• The overall highest quality text analytics
• Support for a number of major (human) languages
• Interfaces available for various major modern programming languages
• Ability to run as a simple web service

Stanford CoreNLP is an integrated framework. Its goal is to make it very easy to apply a bunch of linguistic analysis tools to a piece of text. A CoreNLP tool pipeline can be run on a piece of plain text with just two lines of code. It is designed to be highly flexible and extensible. With a single option you can change which tools should be enabled and which should be disabled. Stanford CoreNLP integrates many of Stanford’s NLP tools, including the part-of-speech (POS) tagger, the named entity recognizer (NER), the parser, the coreference resolution system, sentiment analysis, bootstrapped pattern learning, and the open information extraction tools. Its analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications.

• It’s copy-n-paste, you didn’t have to write it
• It’s appeal to authority (Stanford)
• It’s truthful

The truthful point is a throw-away these days but thought I should mention it. 😉

### Nuremberg Trial Verdicts [70th Anniversary]

Saturday, October 1st, 2016

From the post:

Seventy years ago – on October 1, 1946 – the Nuremberg trial, one of the most prominent trials of the last century, concluded when the International Military Tribunal (IMT) issued the verdicts for the main war criminals of the Second World War. The IMT sentenced twelve of the defendants to death, seven to terms of imprisonment ranging from ten years to life, and acquitted three.

The IMT was established on August 8, 1945 by the United Kingdom (UK), the United States of America, the French Republic, and the Union of Soviet Socialist Republics (U.S.S.R.) for the trial of war criminals whose offenses had no particular geographical location. The defendants were indicted for (1) crimes against peace, (2) war crimes, (3) crimes against humanity, and of (4) a common plan or conspiracy to commit those aforementioned crimes. The trial began on November 20, 1945 and a total of 403 open sessions were held. The prosecution called thirty-three witnesses, whereas the defense questioned sixty-one witnesses, in addition to 143 witnesses who gave evidence for the defense by means of written answers to interrogatories. The hearing of evidence and the closing statements were concluded on August 31, 1946.

The individuals named as defendants in the trial were Hermann Wilhelm Göring, Rudolf Hess, Joachim von Ribbentrop, Robert Ley, Wilhelm Keitel, Ernst Kaltenbrunner, Alfred Rosenberg, Hans Frank, Wilhelm Frick, Julius Streicher, Walter Funk, Hjalmar Schacht, Karl Dönitz, Erich Raeder, Baldur von Schirach, Fritz Sauckel, Alfred Jodl, Martin Bormann, Franz von Papen, Arthur Seyss-Inquart, Albert Speer, Constantin von Neurath, Hans Fritzsche, and Gustav Krupp von Bohlen und Halbach. All individual defendants appeared before the IMT, except for Robert Ley, who committed suicide in prison on October 25, 1945; Gustav Krupp von Bolden und Halbach, who was seriously ill; and Martin Borman, who was not in custody and whom the IMT decided to try in absentia. Pleas of “not guilty” were entered by all the defendants.

The trial record is spread over forty-two volumes, “The Blue Series,” Trial of the Major War Criminals before the International Military Tribunal Nuremberg, 14 November 1945 – 1 October 1946.

All forty-two volumes are available in PDF format and should prove to be a more difficult indexing, mining, modeling, searching challenge than twitter feeds.

Imagine instead of “text” similarity, these volumes were mined for “deed” similarity. Similarity to deeds being performed now. By present day agents.

Instead of seldom visited dusty volumes in the library stacks, “The Blue Series” could develop a sharp bite.

### Jane, John … Leslie? A Historical Method for Algorithmic Gender Prediction [Gatekeeping]

Tuesday, January 5th, 2016

Abstract:

This article describes a new method for inferring the gender of personal names using large historical datasets. In contrast to existing methods of gender prediction that treat names as if they are timelessly associated with one gender, this method uses a historical approach that takes into account how naming practices change over time. It uses historical data to measure the likelihood that a name was associated with a particular gender based on the time or place under study. This approach generates more accurate results for sources that encompass changing periods of time, providing digital humanities scholars with a tool to estimate the gender of names across large textual collections. The article first describes the methodology as implemented in the gender package for the R programming language. It goes on to apply the method to a case study in which we examine gender and gatekeeping in the American historical profession over the past half-century. The gender package illustrates the importance of incorporating historical approaches into computer science and related fields.

An excellent introduction to the gender package for R, historical grounding of the detection of gender by name, with the highlight of the article being the application of this technique to professional literature in American history.

It isn’t uncommon to find statistical techniques applied to texts whose authors and editors are beyond the reach of any critic or criticism.

It is less than common to find statistical techniques applied to extant members of a profession.

Kudos to both Blevins and Mullen for refinement the detection of gender and for applying that refinement publishing in American history.

### Querying Biblical Texts: Part 1 [Humanists Take Note!]

Saturday, November 14th, 2015

From the post:

This is the first in a series on querying Greek texts with XQuery. We will also look at the differences among various representations of the same text, starting with the base text, morphology, and three different treebank formats. As we will see, the representation of a text indicates what the producer of the text was most interested in, and it determines the structure and power of queries done on that particular representation. The principles discussed here also apply to other languages.

This is written as a tutorial, and it can be read in two ways. The first time through, you may want to simply read the text. If you want to really learn how to do this yourself, you should download an XQuery processor and some data (in your favorite biblical language) and try these queries and variations on them.

Humanists need to follow this series and pass it along to others.

Texts of interest to you will vary but the steps Jonathan covers are applicable to all texts (well, depending upon your encoding).

In exchange for learning a little XQuery, you can gain a good degree of mastery over XML encoded texts.

Enjoy!

### Editors’ Choice: An Introduction to the Textreuse Package [+ A Counter Example]

Tuesday, November 10th, 2015

Editors’ Choice: An Introduction to the Textreuse Package by Lincoln Mullen.

From the post:

A number of problems in digital history/humanities require one to calculate the similarity of documents or to identify how one text borrows from another. To give one example, the Viral Texts project, by Ryan Cordell, David Smith, et al., has been very successful at identifying reprinted articles in American newspapers. Kellen Funk and I have been working on a text reuse problem in nineteenth-century legal history, where we seek to track how codes of civil procedure were borrowed and modified in jurisdictions across the United States.

As part of that project, I have recently released the textreuse package for R to CRAN. (Thanks to Noam Ross for giving this package a very thorough open peer review for rOpenSci, to whom I’ve contributed the package.) This package is a general purpose implementation of several algorithms for detecting text reuse, as well as classes and functions for investigating a corpus of texts. Put most simply, full text goes in and measures of similarity come out. (emphasis added)

Kudos to Lincoln on this important contribution to the digital humanities! Not to mention the package will also be useful for researchers who want to compare the “similarity” of texts as “subjects” for purposes of elimination of duplication (called merging in some circles) for presentation to a reader.

I highlighted

Put most simply, full text goes in and measures of similarity come out.

to offer a cautionary tale about the assumption that a high measure of similarity is an indication of the “source” of a text.

Louisiana, my home state, is the only civilian jurisdiction in the United States. Louisiana law, more at one time than now, is based upon Roman law.

Roman law and laws based upon it have a very deep and rich history that I won’t even attempt to summarize.

It is sufficient for present purposes to say the Digest of the Civil Laws now in Force in the Territory of Orleans (online version, English/French) was enacted in 1808.

A scholarly dispute arose (1971-1972) between Professor Batiza (Tulane), who considered the Digest to reflect the French civil code and Professor Pascal (LSU), who argued that despite quoting the French civil code quite liberally, that the redactors intended to codify the Spanish civil law in force at the time of the Louisiana Purchase.

The Batiza vs. Pascal debate was carried out at length and in public:

Batiza, The Louisiana Civil Code of 1808: Its Actual Sources and Present Relevance, 46 TUL. L. REV. 4 (1971); Pascal, Sources of the Digest of 1808: A Reply to Professor Batiza, 46 TUL.L.REV. 603 (1972); Sweeney, Tournament of Scholars over the Sources of the Civil Code of 1808, 46 TUL. L. REV. 585 (1972); Batiza, Sources of the Civil Code of 1808, Facts and Speculation: A Rejoinder, 46 TUL. L. REV. 628 (1972).

I could not find any freely available copies of those articles online. (Don’t encourage paywalls accessing such material. Find it at your local law library.)

There are a couple of secondary articles that discuss the dispute: A.N. Yiannopoulos, The Civil Codes of Louisiana, 1 CIV. L. COMMENT. 1, 1 (2008) at http://www.civil-law.org/v01i01-Yiannopoulos.pdf, and John W. Cairns, The de la Vergne Volume and the Digest of 1808, 24 Tulane European & Civil Law Forum 31 (2009), which are freely available online.

You won’t get the full details from the secondary articles but they do capture some of the flavor of the original dispute. I can report (happily) that over time, Pascal’s position has prevailed. Textual history is more complex than rote counting techniques can capture.

A far more complex case of “text similarity” than Lincoln addresses in the Textreuse package, but once you move beyond freshman/doctoral plagiarism, the “interesting cases” are all complicated.

### Text Mining Meets Neural Nets: Mining the Biomedical Literature

Wednesday, October 28th, 2015

From the webpage:

Text mining and natural language processing employ a range of techniques from syntactic parsing, statistical analysis, and more recently deep learning. This presentation presents recent advances in dense word representations, also known as word embedding, and their advantages over sparse representations, such as the popular term frequency-inverse document frequency (tf-idf) approach. It also discusses convolutional neural networks, a form of deep learning that is proving surprisingly effective in natural language processing tasks. Reference papers and tools are included for those interested in further details. Examples are drawn from the bio-medical domain.

Basically an abstract for the 58 slides you will find here: http://www.slideshare.net/DanSullivan10/text-mining-meets-neural-nets.

The best thing about these slides is the wealth of additional links to other resources. There is only so much you can say on a slide so links to more details should be a standard practice.

Slide 53: Formalize a Mathematical Model of Semantics, seems a bit ambitious to me. Considering mathematics are a subset of natural languages. Difficult to see how the lesser could model the greater.

You could create a mathematical model of some semantics and say it was all that is necessary, but that’s been done before. Always strive to make new mistakes.

### Text Analysis Without Programming

Sunday, October 18th, 2015

My favorite line in the slideshow reads:

PDFs are a sad text data reality

The slides give a good overview of a number of simple tools for text analysis.

And Cherny doesn’t skimp on pointing out issues with tools such as word clouds, where she says:

People don’t know what they indicate (and at the bottom of the slide: “But geez do people love them.”)

I suspect her observation on the uncertainty of what word clouds indicate is partially responsible for their popularity.

No matter what conclusion you draw about a word cloud, how could anyone offer a contrary argument?

A coding talk is promised and I am looking forward to it.

Enjoy!

### Document Summarization via Markov Chains

Saturday, October 17th, 2015

Document Summarization via Markov Chains by Atabey Kaygun.

From the post:

Description of the problem

Today’s question is this: we have a long text and we want a machine generated summary of the text. Below, I will describe a statistical (hence language agnostic) method to do just that.

Sentences, overlaps and Markov chains.

In my previous post I described a method to measure the overlap between two sentences in terms of common words. Today, we will use the same measure, or a variation, to develop a discrete Markov chain whose nodes are labeled by individual sentences appearing in our text. This is essentially page rank applied to sentences.

Atabey says the algorithm (code supplied) works well on:

news articles, opinion pieces and blog posts.

Not so hot on Supreme Court decisions.

In commenting on a story from the New York Times, Obama Won’t Seek Access to Encrypted User Data, I suspect, Atabey says that we have no reference for “what frustrated him” in the text summary.

If you consider the relevant paragraph from the New York Times story:

Mr. Comey had expressed alarm a year ago after Apple introduced an operating system that encrypted virtually everything contained in an iPhone. What frustrated him was that Apple had designed the system to ensure that the company never held on to the keys, putting them entirely in the hands of users through the codes or fingerprints they use to get into their phones. As a result, if Apple is handed a court order for data — until recently, it received hundreds every year — it could not open the coded information.

The reference is clear. Several other people are mentioned in the New York Times article but none rank high enough to appear in the summary.

Not a sure bet but with testing, try attribution to people who rank high enough to appear in the summary.

### International Hysteria Over American Gun Violence

Monday, October 5th, 2015

Australia’s call for a boycott on U.S. travel until gun-reform is passed may be the high point of the international hysteria over gun violence in the United States. Or it may not be. Hard to say at this point.

Social media has been flooded with hand wringing over the loss of “innocent” lives, etc., you know the drill.

The victims in Oregon were no doubt “innocent,” but innocence alone isn’t the criteria by which “mass murder” is judged.

At least not according to both the United States government, other Western governments and their affiliated news organizations.

Take the Los Angeles Times for example, which has an updated list of mass shootings, 1984 – 2015.

Or the breathless prose of The Chicagoist in Chicago Dominates The U.S. In Mass Shootings Count.

Based on data compiled by the crowd-sourced Mass Shooting Tracker site, the Guardian discovered that there were 994 mass shootings—defined as an incident in which four or more people are shot—in 1,004 days since Jan. 1, 2013. The Oregon shooting happened on the 274th day of 2015 and was the 294th mass shooting of the year in the U.S.

Some 294 mass shootings since January 1, 2015 in the U.S.?

Chump change my friend, chump change.

No disrespect to the innocent dead, wounded or their grieving families, but as I said, “innocence isn’t the criteria for judging mass violence. Not by Western governments, not by the Western press.

You will have to do a little data mining to come to that conclusion but if you have the time, follow along.

First, of course, we have to find acts of violence with no warning to its innocent victims who were just going about their lives. At least until pain and death came raining out of the sky.

If you select a country name, your options are Syria and Iraq, a pop-up will display the latest news briefing on “Airstrikes in Iraq and Syria.” Under the current summary, you will see “View Information on Previous Airstrikes.”

Selecting “View Information on Previous Airstrikes” will give you a very long drop down page with previous air strike reports. It doesn’t list human casualties or the number of bombs dropped, but it does recite the number of airstrikes.

Capture that information down to January 1, 2015 and save it to a text file. I have already captured it and you can download us-airstrikes-iraq-syria.txt.

You will notice that the file has text other than the air strikes, but air strikes are reported in a common format:

 - Near Al Hasakah, three strikes struck three separate ISIL tactical units
and destroyed three ISIL structures, two ISIL fighting positions, and an
ISIL motorcycle.
- Near Ar Raqqah, one strike struck an ISIL tactical unit.
- Near Mar’a, one strike destroyed an ISIL excavator.
- Near Washiyah, one strike damaged an ISIL excavator.


I used: grep '\- Near' us-airstrikes-iraq-syria.txt > us-airstrikes-iraq-syria-strikes.txt

Since I now have all the lines with airstrike count data, how do I add up all the numbers?

I am sure there is an XQuery solution but its throw-away data , so I took the easy way out:

grep 'one airstrike' us-airstrikes-iraq-syria-strikes.txt | wc -l

Which gave me a count of all the lines with “one airstrike,” or 629 if you are interested.

Just work your way up through “ten airstrikes” and after that, nothing but zeroes. Multiple the number of lines times the number in the search expression and you have the number of airstrikes for that number. One I found was 132 for “four airstrikes,” so that was 528 airstrikes for that number.

Oh, I forgot to mention, some of the reports don’t use names for numbers but digits. Yeah, inconsistent data.

The dirty answer to that was:

grep '[0-9] airstrikes' us-airstrikes-iraq-syria-strikes.txt > us-airstrikes-iraq-syria-strikes-digits.txt

The “[0-9]” detects any digit, between zero and nine. Could have made it a two-digit number but any two-digit number starts with one digit so why bother?

Anyway, that found another 305 airstrikes that were reported in digits.

Ah, total number of airstrikes, not bombs but airstrikes since January 1, 2015?

4,207 airstrikes as of today.

That’s four thousand, two hundred and seven (minimum, more than one bomb per airstrike), times that innocent civilians may have been murdered or at least terrorized by violence falling out of the sky.

Those 4,207 events were not the work of marginally functional, disturbed or troubled individuals. No, those events were orchestrated by highly trained, competent personnel, backed by the largest military machine on the planet and a correspondingly large military industrial complex.

I puzzle over the international hysteria over American gun violence when the acts are random, unpredictable and departures from the norm. Think of all the people with access to guns in the United States who didn’t go on violent rampages.

The other puzzlement is that the crude data mining I demonstrated above establishes the practice of violence against innocents is a long standing and respected international practice.

Why stress over 294 mass shootings in the U.S. when 4,207 airstrikes in 2015 have killed or endangered equally innocent civilians who are non-U.S. citizens?

What is fair for citizens of one country should be fair for citizens of every country. The international community seems to be rather selective when applying that principle.

### Workflow for R & Shakespeare

Friday, October 2nd, 2015

A new data processing workflow for R: dplyr, magrittr, tidyr, ggplot2

From the post:

Over the last year I have changed my data processing and manipulation workflow in R dramatically. Thanks to some great new packages like dplyr, tidyr and magrittr (as well as the less-new ggplot2) I've been able to streamline code and speed up processing. Up until 2014, I had used essentially the same R workflow (aggregate, merge, apply/tapply, reshape etc) for more than 10 years. I have added a few improvements over the years in the form of functions in packages doBy, reshape2 and plyr and I also flirted with the package data.table (which I found to be much faster for big datasets but the syntax made it difficult to work with) — but the basic flow has remained remarkably similar. Until now…

Given how much I've enjoyed the speed and clarity of the new workflow, I thought I would share a quick demonstration.

In this example, I am going to grab data from a sample SQL database provided by Google via Google BigQuery and then give examples of manipulation using dplyr, magrittr and tidyr (and ggplot2 for visualization).

This is a great introduction to a work flow in R that you can generalize for your own purposes.

Word counts won’t impress your English professor but you will have a base for deeper analysis of Shakespeare.

I first saw this in a tweet by Christophe Lalanne.

### Discovering Likely Mappings between APIs using Text Mining [Likely Merging?]

Monday, September 28th, 2015

Discovering Likely Mappings between APIs using Text Mining by Rahul Pandita, Raoul Praful Jetley, Sithu D Sudarsan, Laurie Williams.

Abstract:

Developers often release different versions of their applications to support various platform/programming-language application programming interfaces (APIs). To migrate an application written using one API (source) to another API (target), a developer must know how the methods in the source API map to the methods in the target API. Given a typical platform or language exposes a large number of API methods, manually writing API mappings is prohibitively resource-intensive and may be error prone. Recently, researchers proposed to automate the mapping process by mining API mappings from existing codebases. However, these approaches require as input a manually ported (or at least functionally similar) code across source and target APIs. To address the shortcoming, this paper proposes TMAP: Text Mining based approach to discover likely API mappings using the similarity in the textual description of the source and target API documents. To evaluate our approach, we used TMAP to discover API mappings for 15 classes across: 1) Java and C# API, and 2) Java ME and Android API. We compared the discovered mappings with state-of-the-art source code analysis based approaches: Rosetta and StaMiner. Our results indicate that TMAP on average found relevant mappings for 57% more methods compared to previous approaches. Furthermore, our results also indicate that TMAP on average found exact mappings for 6.5 more methods per class with a maximum of 21 additional exact mappings for a single class as compared to previous approaches.

From the introduction:

Our intuition is: since the API documents are targeted towards developers, there may be an overlap in the language used to describe similar concepts that can be leveraged.

There are a number of insights in this paper but this statement of intuition alone is enough to justify reading the paper.

What if instead of API documents we were talking about topics that had been written for developers? Isn’t it fair to assume that concepts would have the same or similar vocabularies?

The evidence from this paper certainly suggests that to be the case.

Of course, merging rules would have to allow for “likely” merging of topics, which could then be refined by readers.

Readers who hopefully contribute more information to make “likely” merging more “precise.” (At least in their view.)

That’s one of the problems with most semantic technologies isn’t it?

“Precision” can only be defined from a point of view, which by definition varies from user to user.

What would it look like to allow users to determine their desired degree of semantic precision?

Suggestions?

### Unix™ for Poets

Wednesday, July 29th, 2015

A very delightful take on using basic Unix tools for text processing.

Exercises cover:

1. Count words in a text

2. Sort a list of words in various ways

• ascii order
• dictionary order
• ‘‘rhyming’’ order

3. Extract useful info from a dictionary

4. Compute ngram statistics

5. Make a Concordance

Fifty-three (53) pages of pure Unix joy!

Enjoy!

### Text Processing in R

Wednesday, July 29th, 2015

Text Processing in R by Matthew James Denny.

From the webpage:

This tutorial goes over some basic concepts and commands for text processing in R. R is not the only way to process text, nor is it really the best way. Python is the de-facto programming language for processing text, with a lot of builtin functionality that makes it easy to use, and pretty fast, as well as a number of very mature and full featured packages such as NLTK and textblob. Basic shell scripting can also be many orders of magnitude faster for processing extremely large text corpora — for a classic reference see Unix for Poets. Yet there are good reasons to want to use R for text processing, namely that we can do it, and that we can fit it in with the rest of our analyses. I primarily make use of the stringr package for the following tutorial, so you will want to install it:

Perhaps not the best tool for text processing but if you are inside R and have text processing needs, this will get you started.

### Practical Text Analysis using Deep Learning

Friday, May 1st, 2015

From the post:

Deep Learning has become a household buzzword these days, and I have not stopped hearing about it. In the beginning, I thought it was another rebranding of Neural Network algorithms or a fad that will fade away in a year. But then I read Piotr Teterwak’s blog post on how Deep Learning can be easily utilized for various image analysis tasks. A powerful algorithm that is easy to use? Sounds intriguing. So I decided to give it a closer look. Maybe it will be a new hammer in my toolbox that can later assist me to tackle new sets of interesting problems.

After getting up to speed on Deep Learning (see my recommended reading list at the end of this post), I decided to try Deep Learning on NLP problems. Several years ago, Professor Moshe Koppel gave a talk about how he and his colleagues succeeded in determining an author’s gender by analyzing his or her written texts. They also released a dataset containing 681,288 blog posts. I found it remarkable that one can infer various attributes about an author by analyzing the text, and I’ve been wanting to try it myself. Deep Learning sounded very versatile. So I decided to use it to infer a blogger’s personal attributes, such as age and gender, based on the blog posts.

If you haven’t gotten into deep learning, here’s another opportunity focused on natural language processing. You can follow Michael’s general directions to learn on your own or follow more detailed instructions in his Ipython notebook.

Enjoy!

### q – Text as Data

Tuesday, April 7th, 2015

From the webpage:

q is a command line tool that allows direct execution of SQL-like queries on CSVs/TSVs (and any other tabular text files).

q treats ordinary files as database tables, and supports all SQL constructs, such as WHERE, GROUP BY, JOINs etc. It supports automatic column name and column type detection, and provides full support for multiple encodings.

q’s web site is http://harelba.github.io/q/. It contains everything you need to download and use q in no time.

I’m not looking for an alternative to awk or sed for CSV/TSV files but you may be.

From the examples I suspect it would be “easier” in some sense of the word to teach than either awk or sed.

Give it a try and let me know what you think.

I first saw this in a tweet by Scott Chamberlain.

### Detecting Text Reuse in Nineteenth-Century Legal Documents:…

Thursday, March 12th, 2015

From the post:

How can you track changes in the law of nearly every state in the United States over the course of half a century? How can you figure out which states borrowed laws from one another, and how can you visualize the connections among the legal system as a whole?

Kellen Funk, a historian of American law, is writing a dissertation on how codes of civil procedure spread across the United States in the second half of the nineteenth century. He and I have been collaborating on the digital part of this project, which involves identifying and visualizing the borrowings between these codes. The problem of text reuse is a common one in digital history/humanities projects. In this post I want to describe our methods and lay out some of our preliminary results. To get a fuller picture of this project, you should read the four posts that Kellen has written about his project:

Quite a remarkable project with many aspects that will be relevant to other projects.

Lincoln doesn’t use the term but this would be called textual criticism, if it were being applied to the New Testament. Of course here, Lincoln and Kellen have the original source document and the date of its adoption. New Testament scholars have copies of copies in no particular order and no undisputed evidence of the original text.

Did I mention that all the source code for this project is on Github?

### TM-Gen: A Topic Map Generator from Text Documents

Wednesday, January 21st, 2015

TM-Gen: A Topic Map Generator from Text Documents by Angel L. Garrido, et al.

From the post:

The vast amount of text documents stored in digital format is growing at a frantic rhythm each day. Therefore, tools able to find accurate information by searching in natural language information repositories are gaining great interest in recent years. In this context, there are especially interesting tools capable of dealing with large amounts of text information and deriving human-readable summaries. However, one step further is to be able not only to summarize, but to extract the knowledge stored in those texts, and even represent it graphically.

In this paper we present an architecture to generate automatically a conceptual representation of knowledge stored in a set of text-based documents. For this purpose we have used the topic maps standard and we have developed a method that combines text mining, statistics, linguistic tools, and semantics to obtain a graphical representation of the information contained therein, which can be coded using a knowledge representation language such as RDF or OWL. The procedure is language-independent, fully automatic, self-adjusting, and it does not need manual configuration by the user. Although the validation of a graphic knowledge representation system is very subjective, we have been able to take advantage of an intermediate product of the process to make an experimental
validation of our proposal.

Of particular note on the automatic construction of topic maps:

TM-Gen adds to the topic map the associations between topics found in each sentence. These associations are given by the verbs present in the sentence. TM-Gen performs this task by searching the subject included as topic, and then it adds the verb as its association. Finally, it links its verb complement with the topic and with the association as a new topic.

Depending on the archive one would expect associations between the authors and articles but also topics within articles, to say nothing of date, the publication, etc. Once established, a user can request a view that consists of more or less detail. If not captured, however, more detail will not be available.

There is only a general description of TM-Gen but enough to put you on the way to assembling something quite similar.

### Modelling Plot: On the “conversional novel”

Tuesday, January 20th, 2015

Modelling Plot: On the “conversional novel” by Andrew Piper.

From the post:

I am pleased to announce the acceptance of a new piece that will be appearing soon in New Literary History. In it, I explore techniques for identifying narratives of conversion in the modern novel in German, French and English. A great deal of new work has been circulating recently that addresses the question of plot structures within different genres and how we might or might not be able to model these computationally. My hope is that this piece offers a compelling new way of computationally studying different plot types and understanding their meaning within different genres.

Looking over recent work, in addition to Ben Schmidt’s original post examining plot “arcs” in TV shows using PCA, there have been posts by Ted Underwood and Matthew Jockers looking at novels, as well as a new piece in LLC that tries to identify plot units in fairy tales using the tools of natural language processing (frame nets and identity extraction). In this vein, my work offers an attempt to think about a single plot “type” (narrative conversion) and its role in the development of the novel over the long nineteenth century. How might we develop models that register the novel’s relationship to the narration of profound change, and how might such narratives be indicative of readerly investment? Is there something intrinsic, I have been asking myself, to the way novels ask us to commit to them? If so, does this have something to do with larger linguistic currents within them – not just a single line, passage, or character, or even something like “style” – but the way a greater shift of language over the course of the novel can be generative of affective states such as allegiance, belief or conviction? Can linguistic change, in other words, serve as an efficacious vehicle of readerly devotion?

While the full paper is available here, I wanted to post a distilled version of what I see as its primary findings. It’s a long essay that not only tries to experiment with the project of modelling plot, but also reflects on the process of model building itself and its place within critical reading practices. In many ways, its a polemic against the unfortunate binariness that surrounds debates in our field right now (distant/close, surface/depth etc.). Instead, I want us to see how computational modelling is in many ways conversional in nature, if by that we understand it as a circular process of gradually approaching some imaginary, yet never attainable centre, one that oscillates between both quantitative and qualitative stances (distant and close practices of reading).

Andrew writes of “…critical reading practices….” I’m not sure that technology will increase the use of “…critical reading practices…” but it certainly offers the opportunity to “read” texts in different ways.

I have done this with IT standards but never a novel, attempt reading it from the back forwards, a sentence at a time. At least with authoring you are proofing, it provides a radically different perspective than the more normal front to back. The first thing you notice is that it interrupts your reading/skimming speed so you will catch more errors as well as nuances in the text.

Before you think that literary analysis is a bit far afield from “practical” application, remember that narratives (think literature) are what drive social policy and decision making.

Take the current popular “war on terrorism” narrative that is so popular and unquestioned in the United States. Ask anyone inside the beltway in D.C. and they will blather on and on about the need to defend against terrorism. But there is an absolute paucity of terrorists, at least by deed, in the United States. Why does the narrative persist in the absence of any evidence to support it?

The various Red Scares in U.S. history were similar narratives that have never completely faded. They too had a radical disconnect between the narrative and the “facts on the ground.”

Piper doesn’t offer answers to those sort of questions but a deeper understanding of narrative, such as is found in novels, may lead to hints with profound policy implications.

### A Comparison of Two Unsupervised Table Recognition Methods from Digital Scientific Articles

Monday, January 12th, 2015

A Comparison of Two Unsupervised Table Recognition Methods from Digital Scientific Articles by Stefan Klampfl, Kris Jack, Roman Kern.

Abstract:

In digital scientific articles tables are a common form of presenting information in a structured way. However, the large variability of table layouts and the lack of structural information in digital document formats pose significant challenges for information retrieval and related tasks. In this paper we present two table recognition methods based on unsupervised learning techniques and heuristics which automatically detect both the location and the structure of tables within a article stored as PDF. For both algorithms the table region detection first identifies the bounding boxes of individual tables from a set of labelled text blocks. In the second step, two different tabular structure detection methods extract a rectangular grid of table cells from the set of words contained in these table regions. We evaluate each stage of the algorithms separately and compare performance values on two data sets from different domains. We find that the table recognition performance is in line with state-of-the-art commercial systems and generalises to the non-scientific domain.

Excellent article if you have ever struggled with the endless tables in government documents.

I first saw this in a tweet by Anita de Waard.

### Early English Books Online – Good News and Bad News

Friday, January 2nd, 2015

Early English Books Online

The very good news is that 25,000 volumes from the Early English Books Online collection have been made available to the public!

From the webpage:

The EEBO corpus consists of the works represented in the English Short Title Catalogue I and II (based on the Pollard & Redgrave and Wing short title catalogs), as well as the Thomason Tracts and the Early English Books Tract Supplement. Together these trace the history of English thought from the first book printed in English in 1475 through to 1700. The content covers literature, philosophy, politics, religion, geography, science and all other areas of human endeavor. The assembled collection of more than 125,000 volumes is a mainstay for understanding the development of Western culture in general and the Anglo-American world in particular. The STC collections have perhaps been most widely used by scholars of English, linguistics, and history, but these resources also include core texts in religious studies, art, women’s studies, history of science, law, and music.

Even better news from Sebastian Rahtz Sebastian Rahtz (Chief Data Architect, IT Services, University of Oxford):

The University of Oxford is now making this collection, together with Gale Cengage’s Eighteenth Century Collections Online (ECCO), and Readex’s Evans Early American Imprints, available in various formats (TEI P5 XML, HTML and ePub) initially via the University of Oxford Text Archive at http://www.ota.ox.ac.uk/tcp/, and offering the source XML for community collaborative editing via Github. For the convenience of UK universities who subscribe to JISC Historic Books, a link to page images is also provided. We hope that the XML will serve as the base for enhancements and corrections.

This catalogue also lists EEBO Phase 2 texts, but the HTML and ePub versions of these can only be accessed by members of the University of Oxford.

[Technical note]
Those interested in working on the TEI P5 XML versions of the texts can check them out of Github, via https://github.com/textcreationpartnership/, where each of the texts is in its own repository (eg https://github.com/textcreationpartnership/A00021). There is a CSV file listing all the texts at https://raw.githubusercontent.com/textcreationpartnership/Texts/master/TCP.csv, and a simple Linux/OSX shell script to clone all 32853 unrestricted repositories at https://raw.githubusercontent.com/textcreationpartnership/Texts/master/cloneall.sh

Currently, EEBO-TCP Phase II texts are available to authorized users at partner libraries. Once the project is done, the corpus will be available for sale exclusively through ProQuest for five years. Then, the texts will be released freely to the public.

Can you guess why the public is barred from what are obviously public domain texts?

Because our funding is limited, we aim to key as many different works as possible, in the language in which our staff has the most expertise.

Academic projects are supposed to fund themselves and be self-sustaining. When anyone asks about sustainability of an academic project, ask them when the last time your countries military was “self sustaining?” The U.S. has spent $2.6 trillion on a “war on terrorism” and has nothing to show for it other than dead and injured military personnel, perversion of budgetary policies, and loss of privacy on a world wide scale. It is hard to imagine what sort of life-time access for everyone on Earth could be secured for less than$1 trillion. No more special pricing and contracts if you are in countries A to Zed. Eliminate all that paperwork for publishers and to access all you need is a connection to the Internet. The publishers would have a guaranteed income stream, less overhead from sales personnel, administrative staff, etc. And people would have access (whether used or not) to educate themselves, to make new discoveries, etc.

My proposal does not involve payments to large military contractors or subversion of legitimate governments or imposition of American values on other cultures. Leaving those drawbacks to one side, what do you think about it otherwise?

### Leveraging UIMA in Spark

Wednesday, December 17th, 2014

Leveraging UIMA in Spark by Philip Ogren.

Description:

Much of the Big Data that Spark welders tackle is unstructured text that requires text processing techniques. For example, performing named entity extraction on tweets or sentiment analysis on customer reviews are common activities. The Unstructured Information Management Architecture (UIMA) framework is an Apache project that provides APIs and infrastructure for building complex and robust text analytics systems. A typical system built on UIMA defines a collection of analysis engines (such as e.g. a tokenizer, part-of-speech tagger, named entity recognizer, etc.) which are executed according to arbitrarily complex flow control definitions. The framework makes it possible to have interoperable components in which best-of-breed solutions can be mixed and matched and chained together to create sophisticated text processing pipelines. However, UIMA can seem like a heavy weight solution that has a sprawling API, is cumbersome to configure, and is difficult to execute. Furthermore, UIMA provides its own distributed computing infrastructure and run time processing engines that overlap, in their own way, with Spark functionality. In order for Spark to benefit from UIMA, the latter must be light-weight and nimble and not impose its architecture and tooling onto Spark.

In this talk, I will introduce a project that I started called uimaFIT which is now part of the UIMA project (http://uima.apache.org/uimafit.html). With uimaFIT it is possible to adopt UIMA in a very light-weight way and leverage it for what it does best: text processing. An entire UIMA pipeline can be encapsulated inside a single function call that takes, for example, a string input parameter and returns named entities found in the input string. This allows one to call a Spark RDD transform (e.g. map) that performs named entity recognition (or whatever text processing tasks your UIMA components accomplish) on string values in your RDD. This approach requires little UIMA tooling or configuration and effectively reduces UIMA to a text processing library that can be called rather than requiring full-scale adoption of another platform. I will prepare a companion resource for this talk that will provide a complete, self-contained, working example of how to leverage UIMA using uimaFIT from within Spark.

The necessity of creating light-weight ways to bridge the gaps between applications and frameworks is a signal that every solution is trying to be the complete solution. Since we have different views of what any “complete” solution would look like, wheels are re-invented time and time again. Along with all the parts necessary to use those wheels. Resulting in a tremendous duplication of effort.

A component based approach attempts to do one thing. Doing any one thing well, is challenging enough. (Self-test: How many applications do more than one thing well? Assuming they do one thing well. BTW, for programmers, the test isn’t that other programs fail to do it any better.)

Until more demand results in easy to pipeline components, Philip’s uimaFIT is a great way to incorporate text processing from UIMA into Spark.

Enjoy!

### Some tools for lifting the patent data treasure

Monday, December 15th, 2014

From the post:

…Our work can be summarized as follows:

1. We provide an algorithm that allows researchers to find the duplicates inside Patstat in an efficient way
2. We provide an algorithm to connect Patstat to other kinds of information (CITL, Amadeus)
3. We publish the results of our work in the form of source code and data for Patstat Oct. 2011.

More technically, we used or developed probabilistic supervised machine-learning algorithms that minimize the need for manual checks on the data, while keeping performance at a reasonably high level.

The post has links for source code and data for these three papers:

A flexible, scaleable approach to the international patent “name game” by Mark Huberty, Amma Serwaah, and Georg Zachmann

In this paper, we address the problem of having duplicated patent applicants’ names in the data. We use an algorithm that efficiently de-duplicates the data, needs minimal manual input and works well even on consumer-grade computers. Comparisons between entries are not limited to their names, and thus this algorithm is an improvement over earlier ones that required extensive manual work or overly cautious clean-up of the names.

A scaleable approach to emissions-innovation record linkage by Mark Huberty, Amma Serwaah, and Georg Zachmann

PATSTAT has patent applications as its focus. This means it lacks important information on the applicants and/or the inventors. In order to have more information on the applicants, we link PATSTAT to the CITL database. This way the patenting behaviour can be linked to climate policy. Because of the structure of the data, we can adapt the deduplication algorithm to use it as a matching tool, retaining all of its advantages.

Remerge: regression-based record linkage with an application to PATSTAT by Michele Peruzzi, Georg Zachmann, Reinhilde Veugelers

We further extend the information content in PATSTAT by linking it to Amadeus, a large database of companies that includes financial information. Patent microdata is now linked to financial performance data of companies. This algorithm compares records using multiple variables, learning their relative weights by asking the user to find the correct links in a small subset of the data. Since it is not limited to comparisons among names, it is an improvement over earlier efforts and is not overly dependent on the name-cleaning procedure in use. It is also relatively easy to adapt the algorithm to other databases, since it uses the familiar concept of regression analysis.

Record linkage is a form of merging that originated in epidemiology in the late 1940’s. To “link” (read merge) records across different formats, records were transposed into a uniform format and “linking” characteristics chosen to gather matching records together. A very powerful technique that has been in continuous use and development ever since.

One major different with topic maps is that record linkage has undisclosed subjects, that is the subjects that make up the common format and the association of the original data sets with that format. I assume in many cases the mapping is documented but it doesn’t appear as part of the final work product, thereby rendering the merging process opaque and inaccessible to future researchers. All you can say is “…this is the data set that emerged from the record linkage.”

Sufficient for some purposes but if you want to reduce the 80% of your time that is spent munging data that has been munged before, it is better to have the mapping documented and to use disclosed subjects with identifying properties.

Having said all of that, these are tools you can use now on patents and/or extend them to other data sets. The disambiguation problems addressed for patents are the common ones you have encountered with other names for entities.

If a topic map underlies your analysis, the less time you will spend on the next analysis of the same information. Think of it as reducing your intellectual overhead in subsequent data sets.

Income – Less overhead = Greater revenue for you. 😉

PS: Don’t be confused, you are looking for EPO Worldwide Patent Statistical Database (PATSTAT). Naturally there is a US organization, http://www.patstats.org/ that is just patent litigation statistics.

PPS: Sam Hunting, the source of so many interesting resources, pointed me to this post.

### Sony Pictures Demands That News Agencies Delete ‘Stolen’ Data

Monday, December 15th, 2014

Sony Pictures Demands That News Agencies Delete ‘Stolen’ Data by Michael Cieply and Brooks Barnes.

From the article:

Sony Pictures Entertainment warned media outlets on Sunday against using the mountains of corporate data revealed by hackers who raided the studio’s computer systems in an attack that became public last month.

In a sharply worded letter sent to news organizations, including The New York Times, David Boies, a prominent lawyer hired by Sony, characterized the documents as “stolen information” and demanded that they be avoided, and destroyed if they had already been downloaded or otherwise acquired.

Since I wrote about the foolish accusations against North Korea by Sony, I thought it only fair to warn you that the idlers at Sony have decided to threaten everyone else.

A rather big leap from trash talking about North Korea to accusing the rest of the world of being interested in their incestuous bickering.

I certainly don’t want a copy of their movies, released or unreleased. Too much noise and too little signal for the space they would take. But, since Sony has gotten on its “let’s threaten everybody” hobby-horse, I do hope the location of the Sony documents suddenly appears in many more inboxes. patrick@durusau.net. 😉

How would you display choice snippets and those who uttered them when a webpage loads?

The bitching and catching by Sony are sure signs that something went terribly wrong internally. The current circus is an attempt to distract the public from that failure. Probably a member of management with highly inappropriate security clearance because “…they are important!”

Inappropriate security clearances for management to networks is a sign of poor systems administration. I wonder when that shoe is going to drop?

### Missing From Michael Brown Grand Jury Transcripts

Sunday, December 7th, 2014

What’s missing from the Michael Brown grand jury transcripts? Index pages. For 22 out of 24 volumes of grand jury transcripts, the index page is missing. Here’s the list:

• volume 1 – page 4 missing
• volume 2 – page 4 missing
• volume 3 – page 4 missing
• volume 4 – page 4 missing
• volume 5 – page 4 missing
• volume 6 – page 4 missing
• volume 7 – page 4 missing
• volume 8 – page 4 missing
• volume 9 – page 4 missing
• volume 10 – page 4 missing
• volume 11 – page 4 missing
• volume 12 – page 4 missing
• volume 13 – page 4 missing
• volume 14 – page 4 missing
• volume 15 – page 4 missing
• volume 16 – page 4 missing
• volume 17 – page 4 missing
• volume 18 – page 4 missing
• volume 19 – page 4 missing
• volume 20 – page 4 missing
• volume 21 – page 4 present
• volume 22 – page 4 missing
• volume 23 – page 4 missing
• volume 24 – page 4 present

As you can see from the indexes in volumes 21 and 24, they not terribly useful but better than combing twenty-four volumes (4799 pages of text) to find where a witness testifies.

Someone (court reporter?) made a conscious decision to take action that makes the transcripts harder to user.

Perhaps this is, as they say, “chance.”

Stay tuned for posts later this week that upgrade that to “coincidence” and beyond.

### Documents Released in the Ferguson Case

Tuesday, November 25th, 2014

Documents Released in the Ferguson Case (New York Times)

The New York Times has posted the following documents from the Ferguson case:

• 24 Volumes of Grand Jury Testimony
• 30 Interviews of Witnesses by Law Enforcement Officials
• 23 Forensic and Other Reports
• 254 Photographs

Assume you are interested in organizing these materials for rapid access and cross-linking between them.

1. Accessing Grand Jury Testimony by volume and page number?
2. Accessing Interviews of Witnesses by report and page number?
3. Linking people to reports, testimony and statements?
6. Linking Forensic reports to witness statements and/or testimony?
7. Linking physical evidence into witness statements and/or testimony?
8. Others?

It’s a lot of material so which requirements, these or others, would be your first priority?

It’s not a death march project but on the other hand, you need to get the most valuable tasks done first.

Suggestions?

### Mining Idioms from Source Code

Wednesday, November 19th, 2014

Mining Idioms from Source Code by Miltiadis Allamanis and Charles Sutton.

Abstract:

We present the first method for automatically mining code idioms from a corpus of previously written, idiomatic software projects. We take the view that a code idiom is a syntactic fragment that recurs across projects and has a single semantic role. Idioms may have metavariables, such as the body of a for loop. Modern IDEs commonly provide facilities for manually defining idioms and inserting them on demand, but this does not help programmers to write idiomatic code in languages or using libraries with which they are unfamiliar. We present HAGGIS, a system for mining code idioms that builds on recent advanced techniques from statistical natural language processing, namely, nonparametric Bayesian probabilistic tree substitution grammars. We apply HAGGIS to several of the most popular open source projects from GitHub. We present a wide range of evidence that the resulting idioms are semantically meaningful, demonstrating that they do indeed recur across software projects and that they occur more frequently in illustrative code examples collected from a Q&A site. Manual examination of the most common idioms indicate that they describe important program concepts, including object creation, exception handling, and resource management.

A deeply interesting paper that identifies code idioms without the idioms being specified in advance.

Opens up a path to further investigation of programming idioms and annotation of such idioms.

I first saw this in: Mining Idioms from Source Code – Miltiadis Allamanis a review of a presentation by Felienne Hermans.