2007
DOI: 10.1145/1322391.1322393
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Relation extraction and the influence of automatic named-entity recognition

Abstract: We present an approach for extracting relations between named entities from natural language documents. The approach is based solely on shallow linguistic processing, such as tokenization, sentence splitting, part-of-speech tagging, and lemmatization. It uses a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities. We present the results of experiments on extracting five diff… Show more

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Cited by 30 publications
(30 citation statements)
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“…Miller et al adapted a probabilistic context-free parser for information extraction [Miller et al, 2000]. Giuliano et al thoroughly evaluated the effect of NER on RE [Giuliano et al, 2007]. Alicante and Corazza proposed barrier features, such as N-Grams of POS (Part of Speech), word prefixes and suffixes, hypernyms from WordNet etc.…”
Section: Related Wor Ksmentioning
confidence: 99%
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“…Miller et al adapted a probabilistic context-free parser for information extraction [Miller et al, 2000]. Giuliano et al thoroughly evaluated the effect of NER on RE [Giuliano et al, 2007]. Alicante and Corazza proposed barrier features, such as N-Grams of POS (Part of Speech), word prefixes and suffixes, hypernyms from WordNet etc.…”
Section: Related Wor Ksmentioning
confidence: 99%
“…The Roth and Yih data set is not divided in training and test set. Therefore assessment is performed by following the 5-fold cross validation protocol, as in [Giuliano et al, 2007] [Roth & Yih, 2007] [Kate & Mooney, 2010].…”
Section: Data Setmentioning
confidence: 99%
“…In the past, several works [2], [3], [17], [18], [21] focused on how to identify relevant entities (e.g., person, location and organization) and their relations (e.g., work for, live in and kill) such as work for (person-organization), live in (personlocation) and kill (person-person). In contrast to these previous works, this work focuses on predicate-oriented relations among relevant entities (e.g., person, location and action) and their relationships (e.g., related and unrelated) are identified such as related (person-action), unrelated (personaction), related (action-person), unrelated (action-person), related (action-location) and unrelated (action-location).…”
Section: Predicate-oriented Relation Typesmentioning
confidence: 99%
“…Towards discovery of relations among NEs, named entity extraction as well as other preprocesses such as tokenization, sentence splitting, part-of-speech tagging and lemmatization, are usually applied [18]- [21]. As an early work on relation extraction, Ferrández et al [4] extracted relations based on clause splitting of documents.…”
Section: Introductionmentioning
confidence: 99%
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