Natural Language Processing (NLP) for Software Engineering: Our Eclipse plug-in integrates the Eclipse development environment into the Semantic Assistants architecture. It provides a user interface for offering various Natural Language Processing services to users. In particular, when using Eclipse as a software development environment, you can now offer novel semantic analysis services, such as named entity detection or quality analysis of source code comments, to software developers.

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.

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.