Nigel Collier

Extracting the names of genes and gene products with a hidden Markov model

We report the results of a study into the use of a linear interpolating hidden Markov model (HMM) for the task of extracting technical terminology from MEDLINE abstracts and texts in the molecular-biology domain. This is the first stage in a system that will extract event information for automatically updating biology databases. We trained the HMM entirely with bigrams based on lexical and character features in a relatively small corpus of 100 MEDLINE abstracts that were marked-up by domain experts with term classes such as proteins and DNA. Using cross-validation methods we achieved an F-score of 0.73 and we examine the contribution made by each part of the interpolation model to overcoming data sparseness.

The GENIA project: corpus-based knowledge acquisition and information extraction from genome research papers

We present an outline of the genome information acquisition (GENIA) project for automatically extracting biochemical information from journal papers and abstracts. GENIA will be available over the Internet and is designed to aid in information extraction, retrieval and visualisation and to help reduce information overload on researchers. The vast repository of papers available online in databases such as MEDLINE is a natural environment in which to develop language engineering methods and tools and is an opportunity to show how language engineering can play a key role on the Internet.

A comparison of query translation methods for English-Japanese cross-language information retrieval (poster abstract)

In this paper we report results of an investigation into EnglishJapanese Cross-Language Information Retrieval (CLIR) comparing a number of query translation methods. Results from experiments using the standard BMIR-J2 Japanese collection suggest that full machine translation (MT) can outperform popular dictionary-based query translation methods and further that in this context MT is largely robust to queries with little linguistic structure.