Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9% over state-of-the-art systems on two established information extraction tasks.

/pdf/incorporating-non-local-information-into-information-2upchk9bsy.pdf

Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling

We describe here the JNLPBA shared task of bio-entity recognition using an extended version of the GENIA version 3 named entity corpus of MEDLINE abstracts. We provide background information on the task and present a general discussion of the approaches taken by participating systems.

/pdf/introduction-to-the-bio-entity-recognition-task-at-jnlpba-3dus0pzj35.pdf

Introduction to the bio-entity recognition task at JNLPBA

We describe Web-a-Where, a system for associating geography with Web pages. Web-a-Where locates mentions of places and determines the place each name refers to. In addition, it assigns to each page a geographic focus --- a locality that the page discusses as a whole. The tagging process is simple and fast, aimed to be applied to large collections of Web pages and to facilitate a variety of location-based applications and data analyses.Geotagging involves arbitrating two types of ambiguities: geo/non-geo and geo/geo. A geo/non-geo ambiguity occurs when a place name also has a non-geographic meaning, such as a person name (e.g., Berlin) or a common word (Turkey). Geo/geo ambiguity arises when distinct places have the same name, as in London, England vs. London, Ontario.An implementation of the tagger within the framework of the WebFountain data mining system is described, and evaluated on several corpora of real Web pages. Precision of up to 82% on individual geotags is achieved. We also evaluate the relative contribution of various heuristics the tagger employs, and evaluate the focus-finding algorithm using a corpus pretagged with localities, showing that as many as 91% of the foci reported are correct up to the country level.

/pdf/web-a-where-geotagging-web-content-3pylb00e84.pdf

Web-a-where: geotagging web content

The TREC 2005 Genomics Track featured two tasks, an ad hoc retrieval task and four subtasks in text categorization. The ad hoc retrieval task utilized a 10-year, 4.5-million document subset of the MEDLINE bibliographic database, with 50 topics conforming to five generic topic types. The categorization task used a full-text document collection with training and test sets consisting of about 6,000 biomedical journal articles each. Participants aimed to triage the documents into categories representing data resources in the Mouse Genome Informatics database, with performance assessed via a utility measure.

/pdf/trec-2005-genomics-track-overview-4a2ycgutnk.pdf

TREC 2005 Genomics Track Overview.

Character-level neural network for biomedical named entity recognition.

The accelerating growth in biomedical literature has stimulated activity on automated classification of and information extraction from this literature. The work described here attempts to improve on an earlier classification study associating biological articles to GO codes. It demonstrates the need, under particular assumptions, for more access to full text articles and for the use of Part-of-Speech tagging.

/pdf/classification-from-full-text-a-comparison-of-canonical-fj5rrwrve0.pdf

Classification from full text: a comparison of canonical sections of scientific papers

We describe our participation in both tasks in the 2003 TREC Genomics track. For the primary task we concentrated mainly upon query expansion and species-specific document searching. An analysis of the variance of possible retrieval results suggested that the official TREC-supplied test set is only a crude approximation of the true system performance. The secondary task we treated as an extraction problem, using a maximum entropy scorer trained on GeneRIF sentences as positives and other sentences as negatives. While our results were not always equivalent to the actual GeneRIFs, on biological grounds many of them appeared better descriptors than the GeneRIFs themselves.

Edinburgh-Stanford TREC-2003 Genomics Track.

The TREC Genomics track started in 2003 as theﬁrst domain speciﬁc track of the Text RetrievalCompetition. The aim of the track is to develop var-ious IR tasks speciﬁc to the biomedical ﬁeld. Onetask of the ﬁrst year involved the retrieval of docu-ments given a speciﬁc gene, while the second taskrequired the extraction a brief description of genefunction from documents. This year sees a forayinto ad hoc retrieval and a curation and categoriza-tion task.

TREC Genomics 2004

/pdf/edinburgh-stanford-trec-2003-genomics-track-notebook-paper-3c3ianhe6k.pdf

Edinburgh-Stanford TREC 2003 Genomics Track: Notebook Paper

We describe our use of an existing resource, the Mouse Anatomical Nomenclature, to improve a symbolic interface to anatomically-indexed gene expression data. The goal is to reduce user effort in specifying anatomical structures of interest and increase precision and recall.

Enhanced natural language access to anatomically-indexed data

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