Minimal commitment and full lexical disambiguation

Ruch, Patrick; Baud, Robert H.; Bouillon, Pierrette; Robert, Gilbert

doi:10.3115/1117601.1117626

Cited by 16 publications

(11 citation statements)

References 5 publications

(3 reference statements)

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“…Our shallow parser uses both statistical and manually written patterns, applied at the syntactic level (part-of-speech) of each sentence [24], to identify noun phrase boundaries. The parser concentrates on adjective (A) and noun (N) sequences, such as: [A*] [N*], i.e.…”

Section: Methodsmentioning

confidence: 99%

“…Usually inspired by mutual information measures [23], it requires important volumes of training data, while we aim at designing a data independent system. Therefore, in our systems phrases are based on syntactic parsing [24] rather than statistical analysis. However, let us remark thaz data needed to identify statistical phrases are not of the same kind as those needed for training a classifier: the former approach requires only large corpora, while the latter needs supervision, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Data-poor categorization and passage retrieval for Gene Ontology Annotation in Swiss-Prot

et al. 2005

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Background: In the context of the BioCreative competition, where training data were very sparse, we investigated two complementary tasks: 1) given a Swiss-Prot triplet, containing a protein, a GO (Gene Ontology) term and a relevant article, extraction of a short passage that justifies the GO category assignement; 2) given a Swiss-Prot pair, containing a protein and a relevant article, automatic assignement of a set of categories.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-poor categorization and passage retrieval for Gene Ontology Annotation in Swiss-Prot

et al. 2005

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“…words before and after the misspellings) will be the main parameters of the experiment. The contextual modules we experiment use two types of evidences: a trigram word language model, and a part-of-speech (POS) tagger [5]. These contextual filters are linked together in order to improve the final candidate ranking.…”

Section: Sac '2002 Madrid Spainmentioning

confidence: 99%

Information retrieval and spelling correction

Ruch

2002

Proceedings of the 2002 ACM Symposium on Applied Computing

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In a preliminary study, we show the effect of spelling errors on an ad hoc information retrieval task. Then, we report on the comparison of different strategies for correcting spelling errors resulting in non-existent words. Unlike interactive spelling chec_Jcers, where usually only the lef~ context is available, the system we developed takes advantage of the entire context surrounding misspelling. Moreover, unlike traditional systems, based exclusively on a string-to-string edit distance and a word language model, we explore the use of the part-of-speech for selecting candidates. In conclusion, we show that spelling correction improves by extending the context. The best results are obtained when combining a part-of-speech filter with a word language model, and using both the left and right adjacent contexts.

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“…This sentence reduction step used a part-of-speech tagger [27] and a standard list of 369 stopwords (e.g., so, therefore, however, then, etc.) together with a set of stop phrases (e.g., in contrast to other studies, in this paper, etc.).…”

Section: Common Pre-and Post-processing Strategiesmentioning

confidence: 99%

Features Combination for Extracting Gene Functions from MEDLINE

Ruch

Perret

Savoy

2005

Lecture Notes in Computer Science

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Abstract. This paper describes and evaluates a summarization system that extracts the gene function textual descriptions (called GeneRIF) based on a MedLine record. Inputs for this task include both a locus (a gene in the LocusLink database), and a pointer to a MedLine record supporting the GeneRIF. In the suggested approach we merge two independent phrase extraction strategies. The first proposed strategy (LASt) uses argumentative, positional and structural features in order to suggest a GeneRIF. The second extraction scheme (LogReg) incorporates statistical properties to select the most appropriate sentence as the GeneRIF. Based on the TREC-2003 genomic collection, the basic extraction strategies are already competitive (52.78% for LASt and 52.28% for LogReg, respectively). When used in a combined approach, the extraction task clearly shows improvement, achieving a Dice score of over 55%.

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Minimal commitment and full lexical disambiguation

Cited by 16 publications

References 5 publications

Data-poor categorization and passage retrieval for Gene Ontology Annotation in Swiss-Prot

Data-poor categorization and passage retrieval for Gene Ontology Annotation in Swiss-Prot

Information retrieval and spelling correction

Features Combination for Extracting Gene Functions from MEDLINE

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