Semantic Roles in Grammar Engineering

Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014), pages 81–86, Dublin, Ireland, August 23-24 2014. Semantic Roles in Grammar Engineering Wojciech Jaworski Adam Przepiórkowski Institute of Computer Science, Polish Academy of Sciences ul. Jana Kazimierza 5, 02-668 Warszawa wjaworski@mimuw.edu.pl adamp@ipipan.waw.pl Abstract The aim of this paper is to discuss difficul- ties involved in adopting an existing sys- tem of semantic roles in a grammar engi- neering task. Two typical repertoires of se- mantic roles are considered, namely, Verb- Net and Sowa’s system. We report on ex- periments showing the low inter-annotator agreement when using such systems and suggest that, at least in case of languages with rich morphosyntax, an approximation of semantic roles derived from syntactic (grammatical functions) and morphosyn- tactic (grammatical cases) features of ar- guments may actually be beneficial for ap- plications such as textual entailment. 1 Introduction The modern notion of semantic – or thematic – roles stems from the lexical semantic work of Gruber 1965 (his thematic relations) and Fillmore 1968 (so-called deep cases), and was popularised by Jackendoff 1972, but traces of this concept may already be found in the notion of k¯araka in the writings of the Sanskrit grammarian P¯an.ini (4th century BC); see, e.g., Dowty 1991 for a histori- cal introduction. Fillmore’s deep cases are Agen- tive, Dative, Instrumental, Factive, Locative, Ob- jective, as well as Benefactive, Time and Comi- tative, but many other sets of semantic roles may be found in the literature; for example, Dalrym- ple 2001, p. 206, cites – after Bresnan and Kan- erva 1989 – the following ranked list of thematic roles: Agent, Benefactive, Recipient/Experiencer, Instrument, Theme/Patient, Locative. In Natural Language Processing (NLP), one of the most popular repertoires of se- mantic roles is that of VerbNet (Kipper et al. 2000; http://verbs.colorado.edu/ ~mpalmer/projects/verbnet.html), a valence lexicon of English based on Levin’s (1993) classification of verbs according to the diathesis phenomena they exhibit. The VerbNet webpage states that it contains 3769 lemmata divided into 5257 senses. There are 30 semantic roles used in VerbNet 3.2,1 including such stan- dard roles as Agent, Beneficiary and Instrument, but also more specialised roles such as Asset (for quantities), Material (for stuff things are made of) or Pivot (a theme more central to an event than the theme expressed by another argument). This resource is widely used in NLP, and it was one of the main lexical resources behind the Unified Lexicon of English (Crouch and King, 2005), a part of an LFG-based semantic parser (Crouch and King, 2006) employed in tasks such as question answering (Bobrow et al., 2007a) and textual entailment (Bobrow et al., 2007b). Another system of semantic roles considered here is that developed by Sowa (2000; http: //www.jfsowa.com/krbook/) for the pur- pose of knowledge representation in artificial in- telligence. There are 18 thematic roles proposed in Sowa 2000, p. 508, including standard roles such as Agent, Recipient and Instrument, but also 4 temporal and 4 spatial roles. Unlike in case of VerbNet, there is …

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