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

The Languages of Native North America. Marianne Mithun. Cambridge: Cambridge University Press, 1999. 773 pp.

The Languages of Native North America

For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.

/pdf/literature-mining-for-the-biologist-from-information-25hzxbc9uc.pdf

Literature mining for the biologist: from information retrieval to biological discovery.

The goal of the first BioCreAtIvE challenge (Critical Assessment of Information Extraction in Biology) was to provide a set of common evaluation tasks to assess the state of the art for text mining applied to biological problems. The results were presented in a workshop held in Granada, Spain March 28–31, 2004. The articles collected in this BMC Bioinformatics supplement entitled "A critical assessment of text mining methods in molecular biology" describe the BioCreAtIvE tasks, systems, results and their independent evaluation. BioCreAtIvE focused on two tasks. The first dealt with extraction of gene or protein names from text, and their mapping into standardized gene identifiers for three model organism databases (fly, mouse, yeast). The second task addressed issues of functional annotation, requiring systems to identify specific text passages that supported Gene Ontology annotations for specific proteins, given full text articles. The first BioCreAtIvE assessment achieved a high level of international participation (27 groups from 10 countries). The assessment provided state-of-the-art performance results for a basic task (gene name finding and normalization), where the best systems achieved a balanced 80% precision / recall or better, which potentially makes them suitable for real applications in biology. The results for the advanced task (functional annotation from free text) were significantly lower, demonstrating the current limitations of text-mining approaches where knowledge extrapolation and interpretation are required. In addition, an important contribution of BioCreAtIvE has been the creation and release of training and test data sets for both tasks. There are 22 articles in this special issue, including six that provide analyses of results or data quality for the data sets, including a novel inter-annotator consistency assessment for the test set used in task 2.

/pdf/overview-of-biocreative-critical-assessment-of-information-4d96ci8geb.pdf

Overview of BioCreAtIvE: critical assessment of information extraction for biology

Good automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools We present a maximum-entropy based system incorporating a diverse set of features for identifying gene and protein names in biomedical abstracts This system was entered in the BioCreative comparative evaluation and achieved a precision of 083 and recall of 084 in the "open" evaluation and a precision of 078 and recall of 085 in the "closed" evaluation Central contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge sources including full MEDLINE abstracts and web searches

/pdf/exploring-the-boundaries-gene-and-protein-identification-in-1wvc98073t.pdf

Exploring the boundaries: gene and protein identification in biomedical text.

We present an annotation scheme for information status (IS) in dialogue, and validate it on three Switchboard dialogues. We show that our scheme has good reproducibility, and compare it with previous attempts to code IS and related features. We eventually apply the scheme to 147 dialogues, thus producing a corpus that contains nearly 70,000 NPs annotated for IS and over 15,000 coreference links.

/pdf/an-annotation-scheme-for-information-status-in-dialogue-178nrnypfm.pdf

An Annotation Scheme for Information Status in Dialogue.

We present a maximum entropy-based system for identifying named entities (NEs) in biomedical abstracts and present its performance in the only two biomedical named entity recognition (NER) comparative evaluations that have been held to date, namely BioCreative and Coling BioNLP. Our system obtained an exact match F-score of 83.2% in the BioCreative evaluation and 70.1% in the BioNLP evaluation. We discuss our system in detail, including its rich use of local features, attention to correct boundary identification, innovative use of external knowledge resources, including parsing and web searches, and rapid adaptation to new NE sets. We also discuss in depth problems with data annotation in the evaluations which caused the final performance to be lower than optimal.

/pdf/a-system-for-identifying-named-entities-in-biomedical-text-51jxhti02m.pdf

A System for Identifying Named Entities in Biomedical Text: how Results From two Evaluations Reflect on Both the System and the Evaluations

This paper presents techniques for identifying domain specific web sites that have been implemented as part of the EC-funded R&D project, CROSSMARC The project aims to develop technology for extracting interesting information from domain-specific web pages It is therefore important for CROSSMARC to identify web sites in which interesting domain specific pages reside (focused web crawling) This is the role of the CROSSMARC web crawler

/pdf/domain-specific-web-site-identification-the-crossmarc-1tpl9p0622.pdf

Domain-specific Web site identification: the CROSSMARC focused Web crawler

The NITE XML Toolkit (NXT) provides library support for working with multimodal language corpora. We describe our experiences in using it to study discourse effects on syntactic choice using the parsed Switchboard Corpus as a starting point, as a case study for others who may wish to adopt similar techniques using NXT or one of the other libraries that are beginning to emerge. We discuss conversion into the NXT data format; automatic annotation of markables and of constituent length; hand-annotation of markables for animacy information structure, and coreferential links; and data analysis.

/pdf/using-the-nite-xml-toolkit-on-the-switchboard-corpus-to-43v53furv5.pdf

Using the NITE XML Toolkit on the Switchboard Corpus to study syntactic choice: a case study

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