Evaluating Various Linguistic Features on Semantic Relation Extraction
Machine learning approaches for Information Extraction use different types of features to acquire semantically related terms from free text. These features may contain several kinds of linguistic knowledge: from orthographic or lexical to more complex features, like PoS-tags or syntactic dependencies. In this paper we select four main types of linguistic features and evaluate their performance in a systematic way. Despite the combination of some types of features allows us to improve the f-score of the extraction, we observed that by adjusting the positive and negative ratio of the training examples, we can build high quality classifiers with just a single type of linguistic feature, based on generic lexico-syntactic patterns. Experiments were performed on the Portuguese version of Wikipedia.
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Publicación: Congreso
1624015024072
18 de xuño de 2021
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Machine learning approaches for Information Extraction use different types of features to acquire semantically related terms from free text. These features may contain several kinds of linguistic knowledge: from orthographic or lexical to more complex features, like PoS-tags or syntactic dependencies. In this paper we select four main types of linguistic features and evaluate their performance in a systematic way. Despite the combination of some types of features allows us to improve the f-score of the extraction, we observed that by adjusting the positive and negative ratio of the training examples, we can build high quality classifiers with just a single type of linguistic feature, based on generic lexico-syntactic patterns. Experiments were performed on the Portuguese version of Wikipedia. - Garcia, Marcos and Pablo Gamallo
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