2008 MiningRelationalDataFromText

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Subject Headings: Relation Extraction Algorithm

Notes

Cited By

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Abstract

This paper approaches the relation classification problem in information extraction framework with different machine learning strategies, from strictly supervised to weakly supervised. A number of learning algorithms are presented and empirically evaluated on a standard data set. We show that a supervised SVM classifier using various lexical and syntactic features can achieve competitive classification accuracy. Furthermore, a variety of weakly supervised learning algorithms can be applied to take advantage of large amount of unlabeled data when labeling is expensive. Newly introduced random-subspace-based algorithms demonstrate their empirical advantage over competitors in the context of both active learning and bootstrapping.

3. Problem definition

  • The research problem of this paper is a classification of relations between

entities that co-occur in the same linguistic context. From a database perspective, the task is to determine the appropriate relational table into which one should put a given pair of related entities. To be more precise,

    • We only focus on binary relations, i.e., ones between pairs of entities.
    • We only deal with intra-sentence explicit relations in this study. In other words, the two entity arguments of a relation must occur within

a common syntactic construction, in this case a sentence. The relations also have to be “explicit” in the sense that they should have explicit textual support and do not require further reasoning based on understanding of the context's meaning.

    • The goal is to classify the type of relation between two entities (or, in

other words, to put the entity pair into the correct relational table), given that they are known to be related.

    • It is also assumed that entity recognition already takes place beforehand,

hence all entity-related information is available. Typical entity types defined by ACE include person, organization, location, facility, and geo-political entity (GPEs).

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2008 MiningRelationalDataFromTextZhu ZhangMining relational data from text: From strictly supervised to weakly supervised learninghttp://dx.doi.org/10.1016/j.is.2007.10.00210.1016/j.is.2007.10.002
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