Information extraction for question answering

Jijkoun, Valentin; Rijke, Maarten de; Mur, Jori

doi:10.3115/1220355.1220543

Cited by 30 publications

(5 citation statements)

References 8 publications

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“…Therefore, some of the results of this work agree with Jijkoun et al (2004) and Jiang and Zhai (2007), who had concluded that more complex features might hurt the performance of the classifiers (or increase the recall with a cost in precision). Nevertheless, syntactic information has also been reported as useful in previous works (Kambhatla 2004; Zhou et al 2005; Mintz et al 2009).…”

Section: Discussionsupporting

confidence: 90%

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Exploring the effectiveness of linguistic knowledge for biographical relation extraction

García¹,

Gamallo²

2013

Nat. Lang. Eng.

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Machine learning techniques have been implemented to extract instances of semantic relations using diverse features based on linguistic knowledge, such as tokens, lemmas, PoS-tags, or dependency paths. However, there has been little work aiming to know which of these features works better in the relation extraction task, and less in languages other than English. In this paper, various features representing different levels of linguistic knowledge are systematically evaluated for biographical relation extraction. The effectiveness of these features was measured by training several supervised classifiers that only differ in the type of linguistic knowledge used to define their features. The experiments performed in this paper show that some basic linguistic knowledge (provided by lemmas and their combination in bigrams) behaves better than other complex features, such as those based on syntactic analysis. Furthermore, some feature combinations using different levels of analysis are proposed in order (i) to avoid feature overlapping as well as (ii) to evaluate the use of computationally inexpensive and widespread tools such as tokenization and lemmatization. This paper also describes two new freely available corpora for biographical relation extraction in Portuguese and Spanish, built by means of a distant-supervision strategy. Experiments were performed with five semantic relations and two languages, using these corpora.

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Section: Discussionsupporting

confidence: 90%

“…Jijkoun, De Rijke and Mur (2004) evaluate different surface and syntactic patterns for obtaining pairs for question answering. Their results show that syntactic parsing improves the performance of the question answering systems, despite the fact that the extraction is less precise than that obtained through surface patterns.…”

Section: Related Workmentioning

confidence: 99%

Exploring the effectiveness of linguistic knowledge for biographical relation extraction

García¹,

Gamallo²

2013

Nat. Lang. Eng.

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“…Text segments are extracted as candidate character values if the text segments satisfy relevant linguistic rules. Linguistic rules used in MicroPIE include regular expressions, Part-Of-Speech (POS) tag patterns and syntactic patterns [50]. In some cases, multiple linguistic rules are often integrated in one character extractor to deal with varied textual expressions of a character.…”

Section: Methodsmentioning

confidence: 99%

Microbial phenomics information extractor (MicroPIE): a natural language processing tool for the automated acquisition of prokaryotic phenotypic characters from text sources

et al. 2016

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BackgroundThe large-scale analysis of phenomic data (i.e., full phenotypic traits of an organism, such as shape, metabolic substrates, and growth conditions) in microbial bioinformatics has been hampered by the lack of tools to rapidly and accurately extract phenotypic data from existing legacy text in the field of microbiology. To quickly obtain knowledge on the distribution and evolution of microbial traits, an information extraction system needed to be developed to extract phenotypic characters from large numbers of taxonomic descriptions so they can be used as input to existing phylogenetic analysis software packages.ResultsWe report the development and evaluation of Microbial Phenomics Information Extractor (MicroPIE, version 0.1.0). MicroPIE is a natural language processing application that uses a robust supervised classification algorithm (Support Vector Machine) to identify characters from sentences in prokaryotic taxonomic descriptions, followed by a combination of algorithms applying linguistic rules with groups of known terms to extract characters as well as character states. The input to MicroPIE is a set of taxonomic descriptions (clean text). The output is a taxon-by-character matrix—with taxa in the rows and a set of 42 pre-defined characters (e.g., optimum growth temperature) in the columns. The performance of MicroPIE was evaluated against a gold standard matrix and another student-made matrix. Results show that, compared to the gold standard, MicroPIE extracted 21 characters (50%) with a Relaxed F1 score > 0.80 and 16 characters (38%) with Relaxed F1 scores ranging between 0.50 and 0.80. Inclusion of a character prediction component (SVM) improved the overall performance of MicroPIE, notably the precision. Evaluated against the same gold standard, MicroPIE performed significantly better than the undergraduate students.ConclusionMicroPIE is a promising new tool for the rapid and efficient extraction of phenotypic character information from prokaryotic taxonomic descriptions. However, further development, including incorporation of ontologies, will be necessary to improve the performance of the extraction for some character types.

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“…Our approach for answering factoid questions is largely based on QUARTZ, our QA system used for experiments in the TREC 2003 QA track [7] and the CLEF 2004 Question Answering track [6]. We use an architecture where several streams run in parallel: each is based on a different approach to QA and is a self contained QA system in itself.…”

Section: Factoid Questionsmentioning

confidence: 99%

“…We also implemented a number of simple filtering mechanisms that serve as sanity checks for the answer candidates and performed a number of experiments in our answer justification module. We give an overview of the new components here and refer the reader to [3,4,6,7] for an account of the rest of the system.…”

Section: Factoid Questionsmentioning

confidence: 99%

Using Wikipedia at the TREC QA Track

Ahn,

Jijkoun,

Mishne

et al. 2004

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Information extraction for question answering

Cited by 30 publications

References 8 publications

Exploring the effectiveness of linguistic knowledge for biographical relation extraction

Exploring the effectiveness of linguistic knowledge for biographical relation extraction

Microbial phenomics information extractor (MicroPIE): a natural language processing tool for the automated acquisition of prokaryotic phenotypic characters from text sources

Using Wikipedia at the TREC QA Track

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