Archive for the ‘Corpora’ Category

Metaphor Identification in Large Texts Corpora

Tuesday, May 21st, 2013

Metaphor Identification in Large Texts Corpora by Yair Neuman, Dan Assaf, Yohai Cohen, Mark Last, Shlomo Argamon, Newton Howard, Ophir Frieder. (Neuman Y, Assaf D, Cohen Y, Last M, Argamon S, et al. (2013) Metaphor Identification in Large Texts Corpora. PLoS ONE 8(4): e62343. doi:10.1371/journal.pone.0062343)

Abstract:

Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms’ performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus.

A deep review of current work and promising new algorithms on metaphor identification.

I first saw this in Nat Torkinton’s Four short links: 14 May 2013.

Posted in Corpora, Metaphors | No Comments »

AntConc

Thursday, May 9th, 2013

AntConc by Laurence Anthony.

From the help file:

Concordance

The Concordance tool generates KWIC (key word in context) concordance lines from one or more target texts chosen by the user.

Concordance Plot

The Concordance Plot tool generates an alternative view of search term hits in a corpus compared with the Concordance tool. Here the relative position of each hit in a file is displayed as a line in bar chart. (Search terms can be inputted in an identical way to that in the Concordance Tool.)

File View

The File View tool is used to display the original files of the corpus. It can also be used to search for terms within individual files in a similar way to searches using the Concordance and Concordance Plot tools.

Word Clusters

The Word Clusters tool is used to generate an ordered list of clusters that appear around a search term in the target files listed in the left frame of the main window.

N-Grams

The N-grams tool is used to generate an ordered list of N-grams that appear in the target files listed in the left frame of the main window. N-grams are word N-grams, and therefore, large files will create huge numbers of N-grams. For example, N-grams of size 2 for the sentence “this is a pen”, are ‘this is’, ‘is a’ and ‘a pen’.

Collocates

The Collocates tool is used to generate an ordered list of collocates that appear near a search term in the target files listed in the left frame of the main window.

Word List

The Word List feature is used to generate a list of ordered words that appear in the target files listed in the left frame of the main window.

Keyword List

In addition to generating word lists using the Word List tool, AntConc can compare the words that appear in the target files with the words that appear in a ‘reference corpus’ to generate a list of “Keywords”, that are unusually frequent (or infrequent) in the target files.

The 1.0 version appeared in 2002 and the current beta version is 3.3.5.

Great for exploring texts!

Did I mention it is freeware?

Posted in Clustering, Concordance, Corpora | 1 Comment »

50,000 Lessons on How to Read:…

Friday, April 12th, 2013

50,000 Lessons on How to Read: a Relation Extraction Corpus by Dave Orr, Product Manager, Google Research.

From the post:

One of the most difficult tasks in NLP is called relation extraction. It’s an example of information extraction, one of the goals of natural language understanding. A relation is a semantic connection between (at least) two entities. For instance, you could say that Jim Henson was in a spouse relation with Jane Henson (and in a creator relation with many beloved characters and shows).

The goal of relation extraction is to learn relations from unstructured natural language text. The relations can be used to answer questions (“Who created Kermit?”), learn which proteins interact in the biomedical literature, or to build a database of hundreds of millions of entities and billions of relations to try and help people explore the world’s information.

To help researchers investigate relation extraction, we’re releasing a human-judged dataset of two relations about public figures on Wikipedia: nearly 10,000 examples of “place of birth”, and over 40,000 examples of “attended or graduated from an institution”. Each of these was judged by at least 5 raters, and can be used to train or evaluate relation extraction systems. We also plan to release more relations of new types in the coming months.

Another step in the “right” direction.

This is a human-curated set of relation semantics.

Rather than trying to apply this as a universal “standard,” what if you were to create a similar data set for your domain/enterprise?

Using human curators to create and maintain a set of relation semantics?

Being a topic mappish sort of person, I suggest the basis for their identification of the relationship be explicit, for robust re-use.

But you can repeat the same analysis over and over again if you prefer.

Posted in Associations, Corpora, Natural Language Processing, Relation Extraction | No Comments »

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 »

Developing Linguistic Corpora:…

Thursday, March 7th, 2013

Developing Linguistic Corpora: a Guide to Good Practice edited by Martin Wynne.

From the webpage:

Preface
Martin Wynne (AHDS Literature, Languages and Linguistics, University of Oxford, UK)

Chapter 1
Corpus and Text: Basic Principles
John Sinclair (Tuscan Word Centre)

Chapter 2
Adding Linguistic Annotation
Geoffrey Leech (Lancaster University)

Chapter 3
Metadata for Corpus Work
Lou Burnard (University of Oxford)

Chapter 4
Character Encoding in Corpus Construction
Anthony McEnery and Richard Xiao (Lancaster University)

Chapter 5
Spoken Language Corpora
Paul Thompson (University of Reading)

Chapter 6
Archiving, Distribution and Preservation
Martin Wynne (University of Oxford)

Appendix to chapter one: How to make a corpus
John Sinclair (Tuscan Word Centre)

Bibliography

You are unlikely to need to consult a linguistic corpora or build one most days in topic map practice.

But should the occasion arise, some guidance will be nice to have at hand.

I encountered this resource from the Arts and Humanities Data Service while researching “will” as a non-normative substitute for “shall.”

Posted in Corpora, Linguistic Metadata, Linguistics | No Comments »

European Parliament Proceedings Parallel Corpus 1996-2011

Sunday, November 18th, 2012

European Parliament Proceedings Parallel Corpus 1996-2011

From the webpage:

For a detailed description of this corpus, please read:

Europarl: A Parallel Corpus for Statistical Machine Translation, Philipp Koehn, MT Summit 2005, pdf.

Please cite the paper, if you use this corpus in your work. See also the extended (but earlier) version of the report (ps, pdf).

The Europarl parallel corpus is extracted from the proceedings of the European Parliament. It includes versions in 21 European languages: Romanic (French, Italian, Spanish, Portuguese, Romanian), Germanic (English, Dutch, German, Danish, Swedish), Slavik (Bulgarian, Czech, Polish, Slovak, Slovene), Finni-Ugric (Finnish, Hungarian, Estonian), Baltic (Latvian, Lithuanian), and Greek.

The goal of the extraction and processing was to generate sentence aligned text for statistical machine translation systems. For this purpose we extracted matching items and labeled them with corresponding document IDs. Using a preprocessor we identified sentence boundaries. We sentence aligned the data using a tool based on the Church and Gale algorithm.

Version 7, released in May of 2012, has around 60 million words per language.

Just in case you need a corpus for the EU.

I would be mindful of its parlimentary context. Semantic equivalent or similarity there may not hold true for other contexts.

Posted in Corpora, European Parliment Proceedings Corpus, Natural Language Processing | 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 »

Concept Annotation in the CRAFT corpus

Sunday, August 19th, 2012

Concept Annotation in the CRAFT corpus by Michael Bada, Miriam Eckert, Donald Evans, Kristin Garcia, Krista Shipley, Dmitry Sitnikov, William A. Baumgartner, K. Bretonnel Cohen, Karin Verspoor, Judith A. Blake and Lawrence E. Hunter by BMC Bioinformatics 2012, 13:161 doi:10.1186/1471-2105-13-161.

Abstract:

Background

Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text.

Results

This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement.

Conclusions

As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml.

Lessons on what it takes to create a “gold standard” corpus to advance NLP application development.

What do you think the odds are of “high inter[author] agreement” in the absence of such planning and effort?

Sorry, I meant “high interannotator agreement.”

Guess we have to plan for “low inter[author] agreement.”

Suggestions?

Posted in Bioinformatics, Biomedical, Corpora, Natural Language Processing | 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 »

CRAFT: THE COLORADO RICHLY ANNOTATED FULL TEXT CORPUS

Sunday, August 19th, 2012

CRAFT: THE COLORADO RICHLY ANNOTATED FULL TEXT CORPUS

From the Quick Facts:

  • 67 full text articles
  • >560,000 Tokens
  • >21,000 Sentences
  • ~100,000 concept annotations to 7 different biomedical ontologies/terminologies
    • Chemical Entities of Biological Interest (ChEBI)
    • Cell Type Ontology (CL)
    • Entrez Gene
    • Gene Ontology (biological process, cellular component, and molecular function)
    • NCBI Taxonomy
    • Protein Ontology
    • Sequence Ontology
  • Penn Treebank markup for each sentence
  • Multiple output formats available

Let’s see: 67 articles resulted in 100,000 concept annotations, or about 1,493 per article for seven (7) ontologies/terminologies.

Ready to test this mapping out in your topic map application?

Posted in Bioinformatics, Biomedical, Corpora, 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 »

Parallel Language Corpus Hunting?

Friday, April 27th, 2012

Parallel language corpus hunters, particularly in legal informatics can rejoice!

[A] parallel corpus of all European Union legislation, called the Acquis Communautaire, translated into all 22 languages of the EU nations — has been expanded to include EU legislation from 2004-2010…

If you think semantic impedance in one language is tough, step up and try that across twenty-two (22) languages.

Of course, these countries share something of a common historical context. Imagine the gulf when you move up to languages from other historical contexts.

See: DGT-TM-2011, Parallel Corpus of All EU Legislation in Translation, Expanded to Include Data from 2004-2010 for links and other details.

Posted in Corpora, EU, Language, Linguistics | No Comments »

Corpus-Wide Association Studies

Sunday, March 11th, 2012

Corpus-Wide Association Studies by Mark Liberman.

From the post:

I’ve spent the past couple of days at GURT 2012, and one of the interesting talks that I’ve heard was Julian Brooke and Sali Tagliamonte, “Hunting the linguistic variable: using computational techniques for data exploration and analysis”. Their abstract (all that’s available of the work so far) explains that:

The selection of an appropriate linguistic variable is typically the first step of a variationist analysis whose ultimate goal is to identify and explain social patterns. In this work, we invert the usual approach, starting with the sociolinguistic metadata associated with a large scale socially stratified corpus, and then testing the utility of computational tools for finding good variables to study. In particular, we use the ‘information gain’ metric included in data mining software to automatically filter a huge set of potential variables, and then apply our own corpus reader software to facilitate further human inspection. Finally, we subject a small set of particularly interesting features to a more traditional variationist analysis.

This type of data-mining for interesting patterns is likely to become a trend in sociolinguistics, as it is in other areas of the social and behavioral sciences, and so it’s worth giving some thought to potential problems as well as opportunities.

If you think about it, the social/behavioral sciences are being applied to the results of data mining of user behavior now. Perhaps you can “catch the wave” early on this cycle of research.

Posted in Corpora, Data Mining, Linguistics | No Comments »

BUCC 2012: The Fifth Workshop on Building and Using Comparable Corpora

Saturday, December 31st, 2011

BUCC 2012: The Fifth Workshop on Building and Using Comparable Corpora (Special topic: Language Resources for Machine Translation in Less-Resourced Languages and Domains

Dates:

DEADLINE FOR PAPERS: 15 February 2012
Workshop Saturday, 26 May 2012
Lütfi Kirdar Istanbul Exhibition and Congress Centre
Istanbul, Turkey

Some of the information is from: Call for papers. the main conference site does not (yet) have the call for papers posted. Suggest that you verify dates with conference organizers before making travel arrangements.

From the call for papers:

In the language engineering and the linguistics communities, research in comparable corpora has been motivated by two main reasons. In language engineering, it is chiefly motivated by the need to use comparable corpora as training data for statistical NLP applications such as statistical machine translation or cross-lingual retrieval. In linguistics, on the other hand, comparable corpora are of interest in themselves by making possible inter-linguistic discoveries and comparisons. It is generally accepted in both communities that comparable corpora are documents in one or several languages that are comparable in content and form in various degrees and dimensions. We believe that the linguistic definitions and observations related to comparable corpora can improve methods to mine such corpora for applications of statistical NLP. As such, it is of great interest to bring together builders and users of such corpora.

The scarcity of parallel corpora has motivated research concerning the use of comparable corpora: pairs of monolingual corpora selected according to the same set of criteria, but in different languages or language varieties. Non-parallel yet comparable corpora overcome the two limitations of parallel corpora, since sources for original, monolingual texts are much more abundant than translated texts. However, because of their nature, mining translations in comparable corpora is much more challenging than in parallel corpora. What constitutes a good comparable corpus, for a given task or per se, also requires specific attention: while the definition of a parallel corpus is fairly straightforward, building a non-parallel corpus requires control over the selection of source texts in both languages.

Parallel corpora are a key resource as training data for statistical machine translation, and for building or extending bilingual lexicons and terminologies. However, beyond a few language pairs such as English-French or English-Chinese and a few contexts such as parliamentary debates or legal texts, they remain a scarce resource, despite the creation of automated methods to collect parallel corpora from the Web. To exemplify such issues in a practical setting, this year’s special focus will be on

Language Resources for Machine Translation in Less-Resourced Languages and Domains

with the aim of overcoming the shortage of parallel resources when building MT systems for less-resourced languages and domains, particularly by usage of comparable corpora for finding parallel data within and by reaching out for “hidden” parallel data. Lack of sufficient language resources for many language pairs and domains is currently one of the major obstacles in further advancement of machine translation.

Curious about the use of topic maps in the creation of comparable corpora? Seems like the use of language/domain scopes on linguistic data could result in easier construction of comparable corpora.

Posted in Corpora, Linguistics | No Comments »