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<Paper uid="W06-0508">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics A hybrid approach for extracting semantic relations from texts</Title>
  <Section position="4" start_page="57" end_page="57" type="relat">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> Several approaches have been proposed for the extraction of relations from unstructured sources.</Paragraph>
    <Paragraph position="1"> Recently, they have focused on the use of supervised or unsupervised corpus-based techniques in order to automate the task. A very common approach is based on pattern matching, with patterns composed by subject-verb-object (SVO) tuples. Interesting work has been done on the unsupervised automatic detection of relations from a small number of seed patterns. These are used as a starting point to bootstrap the pattern learning process, by means of semantic similarity measures (Yangarber, 2000; Stevenson, 2004).</Paragraph>
    <Paragraph position="2"> Most of the approaches for relation extraction rely on the mapping of syntactic dependencies, such as SVO, onto semantic relations, using either pattern matching or other strategies, such as probabilistic parsing for trees augmented with annotations for entities and relations (Miller et al, 2000), or clustering of semantically similar syntactic dependencies, according to their selectional restrictions (Gamallo et al., 2002).</Paragraph>
    <Paragraph position="3"> In corpus-based approaches, many variations are found concerning the machine learning techniques used to produce classifiers to judge relation as relevant or non-relevant. (Roth and Yih, 2002), e.g., use probabilistic classifiers with constraints induced between relations and entities, such as selectional restrictions. Based on instances represented by a pair of entities and their position in a shallow parse tree, (Zelenko et al., 2003) use support vector machines and voted perceptron algorithms with a specialized kernel model. Also using kernel methods and support vector machines, (Zhao and Grishman, 2005) combine clues from different levels of syntactic information and applies composite kernels to integrate the individual kernels.</Paragraph>
    <Paragraph position="4"> Similarly to our proposal, the framework presented by (Iria and Ciravegna, 2005) aims at the automation of semantic annotations according to ontologies. Several supervised algorithms can be used on the training data represented through a canonical graph-based data model. The framework includes a shallow linguistic processing step, in which corpora are analyzed and a representation is produced according to the data model, and a classification step, where classifiers run on the datasets produced by the linguistic processing step.</Paragraph>
    <Paragraph position="5"> Several relation extraction approaches have been proposed focusing on the task of ontology learning (Reinberger and Spyns, 2004; Schutz and Buitelaar, 2005; Ciaramita et al., 2005).</Paragraph>
    <Paragraph position="6"> More comprehensive reviews can be found in (Maedche, 2002) and (Gomez-Perez and Manzano-Macho, 2003). These approaches aim to learn non-taxonomic relations between concepts, instead of lexical items. However, in essence, they can employ similar techniques to extract the relations. Additional strategies can be applied to determine whether the relations can be lifted from lexical items to concepts, as well as to determine the most appropriate level of abstraction to describe a relation (e.g. Maedche, 2002).</Paragraph>
    <Paragraph position="7"> In the next section we describe our relation extraction approach, which merges features that have shown to be effective in several of the previous works, in order to achieve more comprehensive and accurate results.</Paragraph>
  </Section>
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