jurafsky&martin_3rdEd_17 (1).pdf

Relations united is a unit of ual partof h a b i h c

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Relations United is a unit of UAL PartOf = { h a , b i , h c , d i } American is a unit of AMR Tim Wagner works for American Airlines OrgAff = { h c , e i } United serves Chicago, Dallas, Denver, and San Francisco Serves = { h a , f i , h a , g i , h a , h i , h a , i i } Figure 21.10 A model-based view of the relations and entities in our sample text. Echocardiography, Doppler Diagnoses Acquired stenosis Wikipedia also offers a large supply of relations, drawn from infoboxes , struc- infoboxes tured tables associated with certain Wikipedia articles. For example, the Wikipedia infobox for Stanford includes structured facts like state = "California" or president = "John L. Hennessy" . These facts can be turned into relations like president-of or located-in . or into relations in a metalanguage called RDF (Resource RDF Description Framework). An RDF triple is a tuple of entity-relation-entity, called a RDF triple subject-predicate-object expression. Here’s a sample RDF triple: subject predicate object Golden Gate Park location San Francisco For example the crowdsourced DBpedia (Bizer et al., 2009) is an ontology de- rived from Wikipedia containing over 2 billion RDF triples. Another dataset from Wikipedia infoboxes, Freebase (Bollacker et al., 2008) , has relations like Freebase people/person/nationality location/location/contains people/person/place-of-birth biology/organism classification WordNet or other ontologies offer useful ontological relations that express hier- archical relations between words or concepts. For example WordNet has the is-a or is-a hypernym relation between classes, hypernym Giraffe is-a ruminant is-a ungulate is-a mammal is-a vertebrate is-a an- imal. . . WordNet also has Instance-of relation between individuals and classes, so that for example San Francisco is in the Instance-of relation with city . Extracting these rela- tions is an important step in extending ontologies or building them for new languages or domains. There are four main classes of algorithms for relation extraction: hand-written patterns , supervised machine learning , semi-supervised , and unsupervised . We’ll introduce each of these in the next four sections.
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21.2 R ELATION E XTRACTION 357 21.2.1 Using Patterns to Extract Relations The earliest and still a common algorithm for relation extraction is the use of lexico- syntactic patterns, first developed by Hearst (1992a) . Consider the following sen- tence: Agar is a substance prepared from a mixture of red algae, such as Ge- lidium, for laboratory or industrial use. Hearst points out that most human readers will not know what Gelidium is, but that they can readily infer that it is a kind of (a hyponym of) red algae , whatever that is. She suggests that the following lexico-syntactic pattern NP 0 such as NP 1 { , NP 2 ..., ( and | or ) NP i } , i 1 (21.2) implies the following semantics 8 NP i , i 1 , hyponym ( NP i , NP 0 ) (21.3) allowing us to infer hyponym ( Gelidium , red algae ) (21.4) NP { , NP } * { , } (and | or) other NP H temples, treasuries, and other important civic buildings NP H such as { NP, } * { (or | and) } NP red algae such as Gelidium such NP H as { NP, } * { (or | and) } NP such authors
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