A Semantic Scattering model for the automatic interpretation of English genitives

Abstract

An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper addresses the automatic classification of the semantic relations expressed by English genitives. A learning model is introduced based on the statistical analysis of the distribution of genitives' semantic relations in a corpus. The semantic and contextual features of the genitive's noun phrase constituents play a key role in the identification of the semantic relation. The algorithm was trained and tested on a corpus of approximately 20,000 sentences and achieved an f-measure of 79.80 per cent for of-genitives, far better than the 40.60 per cent obtained using a Decision Trees algorithm, the 50.55 per cent obtained using a Naive Bayes algorithm, or the 72.13 per cent obtained using a Support Vector Machines algorithm on the same corpus using the same features. The results were similar for s-genitives: 78.45 per cent using Semantic Scattering, 47.00 per cent using Decision Trees, 43.70 per cent using Naive Bayes, and 70.32 per cent using a Support Vector Machines algorithm. The results demonstrate the importance of word sense disambiguation and semantic generalization/specialization for this task. They also demonstrate that different patterns (in our case the two types of genitive constructions) encode different semantic information and should be treated differently in the sense that different models should be built for different patterns.

Comprehensive Supersense Disambiguation of English Prepositions and Possessives

“…Corpus-based computational work on semantic disambiguation specifically of prepositions and possessives 2 falls into two categories: the lexicographic/word sense disambiguation approach (Litkowski andHargraves, 2005, 2007;Litkowski, 2014;Ye and Baldwin, 2007;Saint-Dizier, 2006;Dahlmeier et al, 2009;Tratz and Hovy, 2009;Hovy et al, 2010Hovy et al, , 2011Tratz and Hovy, 2013), and the semantic class approach (Moldovan et al, 2004;Badulescu and Moldovan, 2009;O'Hara and Wiebe, 2009;Roth, 2011, 2013;Schneider et al, 2015Schneider et al, , 2016, see also Müller et al, 2012 for German). The lexicographic approach can capture finer-grained meaning distinctions, at a risk of relying upon idiosyncratic and potentially incomplete dictionary definitions.…”

Beyond Possession Existence: Duration and Co-Possession

“…The goal of these efforts is to identify which relation-out of a predefined inventory-holds between two arguments. For example, Tratz and Hovy (2013) investigate semantic relations realized by English possessive constructions, both Nakov and Hearst (2013) and Tratz and Hovy (2010) consider relations realized by noun compounds such as family estate, and Badulescu and Moldovan (2009) extract relations realized by English genitives. Recently, Blodgett and Schneider (2018) present a corpus of web reviews in which the s-genitive and of-genitive are annotated with semantic labels (or supersenses).…”

Extracting Possessions from Social Media: Images Complement Language

“…Possession relations have primarily been studied in efforts targeting large relation repositories between arguments connected with some lexicosyntactic pattern. Tratz and Hovy (2013) work with 17 semantic relations realized by possessive constructions, Badulescu and Moldovan (2009) with 36 relations realized by genitives, and Nakov and Hearst (2013) and Tratz and Hovy (2010) target noun compounds. Blodgett and Schneider (2018) annotate 50 supersenses (including roles and relations between entities) for possessives.…”