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Semantic Software Lab
Concordia University
Montréal, Canada


Natural Language Processing

The OrganismTagger System

Our open source OrganismTagger is a hybrid rule-based/machine-learning system that extracts organism mentions from the biomedical literature, normalizes them to their scientific name, and provides grounding to the NCBI Taxonomy database. Our pipeline provides the flexibility of annotating the species of particular interest to bio-engineers on different corpora, by optionally including detection of common names, acronyms, and strains. The OrganismTagger performance has been evaluated on two manually annotated corpora, OT and Linneaus. On the OT corpus, the OrganismTagger achieves a precision and recall of 95% and 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linneaus-100, the results show a precision and recall of 99% and 97% and grounding with an accuracy of 97.4%. It is described in detail in our publication, Naderi, N., T. Kappler, C. J. O. Baker, and R. Witte, "OrganismTagger: Detection, normalization, and grounding of organism entities in biomedical documents", Bioinformatics, vol. 27, no. 19 Oxford University Press, pp. 2721--2729, August 9, 2011.

New Book Chapter on Semantic Wikis and Natural Language Processing for Cultural Heritage Data

Springer just published a new book, Language Technology for Cultural Heritage, where we also contributed a chapter: "Integrating Wiki Systems, Natural Language Processing, and Semantic Technologies for Cultural Heritage Data Management". The book collects selected, extended papers from several years of the LaTeCH workshop series, where we presented our work on the Durm Project back in 2008.

In this project, which ran from 2004–2006, we analysed the historic Encyclopedia of Architecture, which was written in German between 1880-1943. It was one of the largest projects aiming at conserving all architectural knowledge available at that time. Today, its vast amount of content is mostly lost: few complete sets are available and its complex structure does not lend itself easily to contemporary application. We were able to track down one of the rare complete sets in the Karlsruhe University's library, where it fills several meters of shelves in the archives. The goal, then, was to apply "modern" (as of 2005) semantic technologies to make these heritage documents accessible again by transforming them into a semantic knowledge base (due to funding limitations, we only worked with one book in this project, but the system was developed to be able to eventually cover the complete set). Using techniques from Natural Language Processing and Semantic Computing, we automatically populate an ontology that can be used for various application scenarios: Building historians can use it to navigate and query the encyclopedia, while architects can directly integrate it into contemporary construction tools. Additionally, we made all content accessible through a user-friendly Wiki interface, which combines original text with NLP-derived metadata and adds annotation capabilities for collaborative use (note that not all features are enabled in the public demo version).

All data created in the project (scanned book images, generated corpora, etc.) is publicly available under open content licenses. We also still maintain a number of open source tools that were originally developed for this project, such as the Durm German Lemmatizer. A new version of our Wiki/NLP integration, which will allow everyone to easily set up a similar system, is currently under development and will be available early 2012.

Predicate-Argument EXtractor (PAX)

Krestel, R., R. Witte, and S. Bergler, "Predicate-Argument EXtractor (PAX)", New Challenges for NLP Frameworks, Valletta, Malta : ELRA, pp. 51--54, May 22, 2010.

Flexible Ontology Population from Text: The OwlExporter

Witte, R., N. Khamis, and J. Rilling, "Flexible Ontology Population from Text: The OwlExporter", International Conference on Language Resources and Evaluation (LREC), Valletta, Malta : ELRA, pp. 3845--3850, May 19--21, 2010.

{Automatic Quality Assessment of Source Code Comments: The JavadocMiner}

Hopfe, C. J., Y. Rezgui, E. Métais, A. D. Preece, and H. Li (Eds.), Khamis, N., R. Witte, and J. Rilling, "{Automatic Quality Assessment of Source Code Comments: The JavadocMiner}", 15th International Conference on Applications of Natural Language to Information Systems (NLDB 2010), vol. 6177/2010, Cardiff, UK : Springer, pp. 68--79, June 23--25, 2010.

Text Mining: Wissensgewinnung aus natürlichsprachigen Dokumenten

Witte, R., and J. Mülle (Eds.), Text Mining: Wissensgewinnung aus natürlichsprachigen Dokumenten, Universität Karlsruhe, Fakultät für Informatik, Institut für Programmstrukturen und Datenorganisation (IPD), 2006.
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