Archive for the ‘Corpus Linguistics’ Category

GroningenMeaningBank (GMB)

Thursday, April 11th, 2013

GroningenMeaningBank (GMB)

From the “about” page:

The Groningen Meaning Bank consists of public domain English texts with corresponding syntactic and semantic representations.

Key features

The GMB supports deep semantics, opening the way to theoretically grounded, data-driven approaches to computational semantics. It integrates phenomena instead of covering single phenomena in isolation. This provides a better handle on explaining dependencies between various ambiguous linguistic phenomena, including word senses, thematic roles, quantifier scrope, tense and aspect, anaphora, presupposition, and rhetorical relations. In the GMB texts are annotated rather than
isolated sentences, which provides a means to deal with ambiguities on the sentence level that require discourse context for resolving them.

Method

The GMB is being built using a bootstrapping approach. We employ state-of-the-art NLP tools (notably the C&C tools and Boxer) to produce a reasonable approximation to gold-standard annotations. From release to release, the annotations are corrected and refined using human annotations coming from two main sources: experts who directly edit the annotations in the GMB via the Explorer, and non-experts who play a game with a purpose called Wordrobe.

Theoretical background

The theoretical backbone for the semantic annotations in the GMB is established by Discourse Representation Theory (DRT), a formal theory of meaning developed by the philosopher of language Hans Kamp (Kamp, 1981; Kamp and Reyle, 1993). Extensions of the theory bridge the gap between theory and practice. In particular, we use VerbNet for thematic roles, a variation on ACE‘s named entity classification, WordNet for word senses and Segmented DRT for rhetorical relations (Asher and Lascarides, 2003). Thanks to the DRT backbone, all these linguistic phenomena can be expressed in a first-order language, enabling the practical use of first-order theorem provers and model builders.

Step back towards the source of semantics (that would be us).

One practical question is how to capture semantics for a particular domain or enterprise?

Another is what to capture to enable the mapping of those semantics to those of other domains or enterprises?

Posted in Corpora, Corpus Linguistics, Linguistics, Semantics | No Comments »

…Wikilinks Corpus With 40M Mentions And 3M Entities

Saturday, March 9th, 2013

Google Research Releases Wikilinks Corpus With 40M Mentions And 3M Entities by Frederic Lardinois.

From the post:

Google Research just launched its Wikilinks corpus, a massive new data set for developers and researchers that could make it easier to add smart disambiguation and cross-referencing to their applications. The data could, for example, make it easier to find out if two web sites are talking about the same person or concept, Google says. In total, the corpus features 40 million disambiguated mentions found within 10 million web pages. This, Google notes, makes it “over 100 times bigger than the next largest corpus,” which features fewer than 100,000 mentions.

For Google, of course, disambiguation is something that is a core feature of the Knowledge Graph project, which allows you to tell Google whether you are looking for links related to the planet, car or chemical element when you search for ‘mercury,’ for example. It takes a large corpus like this one and the ability to understand what each web page is really about to make this happen.

Details follow on how to create this data set.

Very cool!

The only caution is that your entities, those specific to your enterprise, are unlikely to appear, even in 40M mentions.

But the Wikilinks Corpus + your entities, now that is something with immediate ROI for your enterprise.

Posted in Corpus Linguistics, Disambiguation, Entities, Entity Resolution | No Comments »

Using information retrieval technology for a corpus analysis platform

Wednesday, September 26th, 2012

Using information retrieval technology for a corpus analysis platform by Carsten Schnober.

Abstract:

This paper describes a practical approach to use the information retrieval engine Lucene for the corpus analysis platform KorAP, currently being developed at the Institut für Deutsche Sprache (IDS Mannheim). It presents a method to use Lucene’s indexing technique and to exploit it for linguistically annotated data, allowing full flexibility to handle multiple annotation layers. It uses multiple indexes and MapReduce techniques in order to keep KorAP scalable.

The support for multiple annotation layers is of particular interest to me because the “subjects” of interest in a text may vary from one reader to another.

Being mindful that for topic maps, the annotation layers and annotations themselves may be subjects for some purposes.

Posted in Corpora, Corpus Linguistics, Information Retrieval, Lucene, MapReduce | 1 Comment »

Gold Standard (or Bronze, Tin?)

Sunday, August 19th, 2012

A corpus of full-text journal articles is a robust evaluation tool for revealing differences in performance of biomedical natural language processing tools by Karin M Verspoor, Kevin B Cohen, Arrick Lanfranchi, Colin Warner, Helen L Johnson, Christophe Roeder, Jinho D Choi, Christopher Funk, Yuriy Malenkiy, Miriam Eckert, Nianwen Xue, William A Baumgartner, Michael Bada, Martha Palmer and Lawrence E Hunter. BMC Bioinformatics 2012, 13:207 doi:10.1186/1471-2105-13-207.

Abstract:

Background

We introduce the linguistic annotation of a corpus of 97 full-text biomedical publications, known as the Colorado Richly Annotated Full Text (CRAFT) corpus. We further assess the performance of existing tools for performing sentence splitting, tokenization, syntactic parsing, and named entity recognition on this corpus.

Results

Many biomedical natural language processing systems demonstrated large differences between their previously published results and their performance on the CRAFT corpus when tested with the publicly available models or rule sets. Trainable systems differed widely with respect to their ability to build high-performing models based on this data.

Conclusions

The finding that some systems were able to train high-performing models based on this corpus is additional evidence, beyond high inter-annotator agreement, that the quality of the CRAFT corpus is high. The overall poor performance of various systems indicates that considerable work needs to be done to enable natural language processing systems to work well when the input is full-text journal articles. The CRAFT corpus provides a valuable resource to the biomedical natural language processing community for evaluation and training of new models for biomedical full text publications.

This is the article that I discovered and then worked my way to it from BioNLP.

Important as a deeply annotated text corpus.

But also a reminder that human annotators created the “gold standard,” against which other efforts are judged.

If you are ill, do you want gold standard research into the medical literature (which involves librarians)? Or is bronze or tin standard research good enough?

PS: I will be going back to pickup the other resources as appropriate.

Posted in Bioinformatics, Biomedical, Corpora, Corpus Linguistics, Natural Language Processing | No Comments »

GATE Teamware: Collaborative Annotation Factories (HOT!)

Wednesday, May 9th, 2012

GATE Teamware: Collaborative Annotation Factories

From the webpage:

Teamware is a web-based management platform for collaborative annotation & curation. It is a cost-effective environment for annotation and curation projects, enabling you to harness a broadly distributed workforce and monitor progress & results remotely in real time.

It’s also very easy to use. A new project can be up and running in less than five minutes. (As far as we know, there is nothing else like it in this field.)

GATE Teamware delivers a multi-function user interface over the Internet for viewing, adding and editing text annotations. The web-based management interface allows for project set-up, tracking, and management:

  • Loading document collections (a “corpus” or “corpora”)
  • Creating re-usable project templates
  • Initiating projects based on templates
  • Assigning project roles to specific users
  • Monitoring progress and various project statistics in real time
  • Reporting of project status, annotator activity and statistics
  • Applying GATE-based processing routines (automatic annotations or post-annotation processing)

I have known about the GATE project in general for years and came to this site after reading: Crowdsourced Legal Case Annotation.

Could be the basis for annotations that are converted into a topic map, but…, I have been a sysadmin before. Maintaining servers, websites, software, etc. Great work, interesting work, but not what I want to be doing now.

Then I read:

Where to get it? The easiest way to get started is to buy a ready-to-run Teamware virtual server from GATECloud.net.

Not saying it will or won’t meet your particular needs, but, certainly is worth a “look see.”

Let me know if you take the plunge!

Posted in Annotation, Cloud Computing, Collaboration, Corpora, Corpus Linguistics, Curation, Entity Resolution, Linguistics | No Comments »

The communicative function of ambiguity in language

Friday, January 20th, 2012

The communicative function of ambiguity in language by Steven T. Piantadosi, Harry Tily and Edward Gibson. (Cognition, 2011) (PDF file)

Abstract:

We present a general information-theoretic argument that all efficient communication systems will be ambiguous, assuming that context is informative about meaning. We also argue that ambiguity allows for greater ease of processing by permitting efficient linguistic units to be re-used. We test predictions of this theory in English, German, and Dutch. Our results and theoretical analysis suggest that ambiguity is a functional property of language that allows for greater communicative efficiency. This provides theoretical and empirical arguments against recent suggestions that core features of linguistic systems are not designed for communication.

This is a must read paper if you are interesting in ambiguity and similar issues.

At page 289, the authors report:

These findings suggest that ambiguity is not enough of a problem to real-world communication that speakers would make much effort to avoid it. This may well be because actual language in context provides other information that resolves the ambiguities most of the time.

I don’t know if our communication systems are efficient or not but I think the phrase “in context” is covering up a very important point.

Our communication systems came about in very high-bandwidth circumstances. We were in the immediate presence of a person speaking. With all the context that provides.

Even if we accept an origin of language of say 200,000 years ago, written language, which provides the basis for communication without the presence of another person, emerges only in the last five or six thousand years. Just to keep it simple, 5 thousand years would be 2.5% of the entire history of language.

So for 97.5% of the history of language, it has been used in a high bandwidth situation. No wonder it has yet to adapt to narrow bandwidth situations.

If writing puts us into a narrow bandwidth situation and ambiguity, where does that leave our computers?

Posted in Ambiguity, Context, Corpus Linguistics, Linguistics | 1 Comment »

Creating a Live, Public Short Message Service Corpus: The NUS SMS Corpus

Wednesday, December 14th, 2011

Creating a Live, Public Short Message Service Corpus: The NUS SMS Corpus by Tao Chen, Min-Yen Kan.

Abstract:

Short Message Service (SMS) messages are largely sent directly from one person to another from their mobile phones. They represent a means of personal communication that is an important communicative artifact in our current digital era. As most existing studies have used private access to SMS corpora, comparative studies using the same raw SMS data has not been possible up to now. We describe our efforts to collect a public SMS corpus to address this problem. We use a battery of methodologies to collect the corpus, paying particular attention to privacy issues to address contributors’ concerns. Our live project collects new SMS message submissions, checks their quality and adds the valid messages, releasing the resultant corpus as XML and as SQL dumps, along with corpus statistics, every month. We opportunistically collect as much metadata about the messages and their sender as possible, so as to enable different types of analyses. To date, we have collected about 60,000 messages, focusing on English and Mandarin Chinese.

A unique and publicly available corpus of material.

Your average marketing company might not have an SMS corpus for you to work with but I can think of some other organizations that do. ;-) Train on this one to win your spurs.

Posted in Corpus Linguistics, Dataset | No Comments »

The web of topics: discovering the topology of topic evolution in a corpus

Thursday, March 31st, 2011

The web of topics: discovering the topology of topic evolution in a corpus by Yookyung Jo, John E. Hopcroft, and, Carl Lagoze, Cornell University, Ithaca, NY, USA.

Abstract:

In this paper we study how to discover the evolution of topics over time in a time-stamped document collection. Our approach is uniquely designed to capture the rich topology of topic evolution inherent in the corpus. Instead of characterizing the evolving topics at fixed time points, we conceptually define a topic as a quantized unit of evolutionary change in content and discover topics with the time of their appearance in the corpus. Discovered topics are then connected to form a topic evolution graph using a measure derived from the underlying document network. Our approach allows inhomogeneous distribution of topics over time and does not impose any topological restriction in topic evolution graphs. We evaluate our algorithm on the ACM corpus. The topic evolution graphs obtained from the ACM corpus provide an effective and concrete summary of the corpus with remarkably rich topology that are congruent to our background knowledge. In a finer resolution, the graphs reveal concrete information about the corpus that were previously unknown to us, suggesting the utility of our approach as a navigational tool for the corpus.

The term topic is being used in this paper to mean a subject in topic map parlance.

From the paper:

Our work is built on the premise that the words relevant to a topic are distributed over documents such that the distribution is correlated with the underlying document network such as a citation network. Specifically, in our topic discovery methodology, in order to test if a multinomial word distribution derived from a document constitutes a new topic, the following heuristic is used. We check that the distribution is exclusively correlated to the document network by requiring it to be significantly present in other documents that are network neighbors of the given document while suppressing the nondiscriminative words using the background model.

Navigation of a corpus on the basis of such a process would indeed be rich, but it would be even richer were multiple ways to represent the same subjects mapped together.

It would also be interesting to see how the resulting graphs, which included only the document titles and abstracts, compared to graphs constructed using the entire documents.

Posted in Corpus Linguistics, Graphs, Navigation | No Comments »

Trending Terms in Google’s Book Corpus – Post

Tuesday, December 28th, 2010

Trending Terms in Google’s Book Corpus

Matthew Hurst covers an interesting new tool at Google Book Corpus that allows tracking of terms over time.

Questions:

  1. Pick at least 3 pairs of terms to track through this interface. (3-5 pages, no citations)
  2. Track only one term over 300 years of publication with one example of usage for every 30 years. (3-5 pages, citations)
  3. What similarity measures would you use to detect variation in the semantic of that term in a corpus covering 300 years? (3-5 pages, citations)

Posted in Corpus Linguistics, Usage | No Comments »

Python Text Processing with NLTK Cookbook

Saturday, December 4th, 2010

Python Text Processing with NLTK Cookbook by Jacob Perkins.

Contents:

  • Chapter 1: Tokenizing Text and WordNet Basics
  • Chapter 2: Replacing and Correcting Words
  • Chapter 3: Creating Custom Corpora
  • Chapter 4: Part-of-Speech Tagging
  • Chapter 5: Extracting Chunks
  • Chapter 6: Transforming Chunks and Trees
  • Chapter 7: Text Classification
  • Chapter 8: Distributed Processing and Handling Large Datasets
  • Chapter 9: Parsing Specific Data
  • Appendix: Penn Treebank Part-of-Speech Tags
  • Index

A sample chapter, Chapter 3: Creating Custom Corpora is available for downloading.

Please post a link to your review of this work.

Even better, send me a copy and I will post a review. (I’m listed on Amazon.)

Posted in Clustering, Co-Words, Corpus Linguistics, Data Mining, Full-Text Search, Linguistic Metadata, Natural Language Processing, Text Analytics | 4 Comments »

Building Concept Structures/Concept Trails

Thursday, December 2nd, 2010

Automatically Building Concept Structures and Displaying Concept Trails for the Use in Brainstorming Sessions and Content Management Systems Authors: Christian Biemann, Karsten Böhm, Gerhard Heyer and Ronny Melz

Abstract:

The automated creation and the visualization of concept structures become more important as the number of relevant information continues to grow dramatically. Especially information and knowledge intensive tasks are relying heavily on accessing the relevant information or knowledge at the right time. Moreover the capturing of relevant facts and good ideas should be focused on as early as possible in the knowledge creation process.

In this paper we introduce a technology to support knowledge structuring processes already at the time of their creation by building up concept structures in real time. Our focus was set on the design of a minimal invasive system, which ideally requires no human interaction and thus gives the maximum freedom to the participants of a knowledge creation or exchange processes. The initial prototype concentrates on the capturing of spoken language to support meetings of human experts, but can be easily adapted for the use in Internet communities that have to rely on knowledge exchange using electronic communication channel.

I don’t share the author’s confidence that corpus linguistics are going to provide the level of accuracy expected.

But, I find the notion of a dynamic semantic map that grows, changes and evolves during a discussion to be intriguing.

This article was published in 2006 so I will follow up to see what later results have been reported.

Posted in Authoring Topic Maps, Co-Words, Collocation, Corpus Linguistics, Mapping, Semantic Diversity, Semantics | No Comments »

Indexing and Querying Linguistic Metadata and Document Content

Wednesday, November 10th, 2010

Indexing and Querying Linguistic Metadata and Document Content Authors: Niraj Aswani, Valentin Tablan, Kalina Bontcheva, Hamish Cunningham

Abstract:

The need for efficient corpus indexing and querying arises frequently both in machine learning-based and human-engineered natural language processing systems. This paper presents the ANNIC system, which can index documents not only by content, but also by their linguististic annotations and features. It also enables users to formulate versatile queries mixing keywords and linguistic information. The result consists of the matching texts in the corpus, displayed within the context of linguistic annotations (not just text, as is customary for KWIC systems). The data is displayed in a graphical user interface, which facilitates its exploration and the discovery of new patterns, which can in turn be tested by launching new ANNIC queries.

Lucene is modified to create an index of a term that includes additional information linguistic information.

Recognizes that search components don’t have to been entirely hand crafted nor exclusively machine learning. (Is there a lesson for topic maps here?)

ANNIC (ANNotations-In-Context), part of GATE (General Architecture for Text Engineering), is a corpus exploration tool.

As topic maps address more sophisticated text bases, such tools will become increasingly important.

Posted in Corpus Linguistics, Linguistic Metadata, Lucene | 1 Comment »