Extracting Noun Phrases for All of MEDLINE
AMIA '99 (American Medical Informatics Assoc) Conf, Washington, DC, Nov. (to appear)

Abstract: A natural language parser that could extract noun phrases for all medical texts would be of great utility in analyzing content for information retrieval. We discuss the extraction of noun phrases from MEDLINE, using a general parser not tuned specifically for any medical domain. The noun phrase extractor is made up of three modules: tokenization; part-of-speech tagging; noun phrase identification. Using our program, we extracted noun phrases from the entire MEDLINE collection, encompassing 9.3 million abstracts. Over 270 million noun phrases were generated, of which 45 million were unique. The quality of these phrases was evaluated by examining all phrases from a sample collection of abstracts. The precision and recall of the phrases from our general parser compared favorably with those from three other parsers we had previously evaluated. We are continuing to improve our parser and evaluate our claim that a generic parser can effectively extract all the different phrases across the entire medical literature.

Semantic Indexing for a Complete Subject Discipline
4th Int. ACM Conf on Digital Libraries, Berkeley, CA, Aug., 1999.

Abstract: As part of the Illinois Digital Library Initiative (DLI) project we developed ``scalable semantics'' technologies. These statistical techniques enabled us to index large collections for deeper search than word matching. Through the auspices of the DARPA Information Management program, we are developing an integrated analysis environment, the Interspace Prototype, that uses "semantic indexing'' as the foundation for supporting concept navigation. These semantic indexes record the contextual correlation of noun phrases, and are computed generically, independent of subject domain.

Using this technology, we were able to compute semantic indexes for a subject discipline. In particular, in the summer of 1998, we computed concept spaces for 9.3M MEDLINE bibliographic records from the National Library of Medicine (NLM) which extensively covered the biomedical literature for the period from 1966 to 1997. In this experiment, we first partitioned the collection into smaller collections (repositories) by subject, extracted noun phrases from titles and abstracts, then performed semantic indexing on these sub-collections by creating a concept space for each repository. The computation required 2 days on a 128-node SGI/CRAY Origin 2000 at the National Center for Supercomputer Applications (NCSA). This experiment demonstrated the feasibility of scalable semantics techniques for large collections. With the rapid increase in computing power, we believe this indexing technology will shortly be feasible on personal computers.