Verbs Taking Clausal and Non-Finite Arguments as Signals of Modality – Revisiting the Issue of Meaning Grounded in Syntax

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 811–822, Berlin, Germany, August 7-12, 2016. c⃝2016 Association for Computational Linguistics Verbs Taking Clausal and Non-Finite Arguments as Signals of Modality – Revisiting the Issue of Meaning Grounded in Syntax Judith Eckle-Kohler Research Training Group AIPHES and UKP Lab Computer Science Department, Technische Universit¨at Darmstadt www.aiphes.tu-darmstadt.de, www.ukp.tu-darmstadt.de Abstract We revisit Levin’s theory about the correspondence of verb meaning and syntax and infer semantic classes from a large syntactic classification of more than 600 German verbs taking clausal and non-finite arguments. Grasping the meaning components of Levin-classes is known to be hard. We address this chal- lenge by setting up a multi-perspective semantic characterization of the inferred classes. To this end, we link the inferred classes and their English translation to independently constructed semantic classes in three different lexicons – the German wordnet GermaNet, VerbNet and FrameNet – and perform a detailed analysis and evaluation of the resulting German–English classification (avail- able at www.ukp.tu-darmstadt. de/modality-verbclasses/). 1 Introduction Verbs taking clausal and non-finite arguments add a further meaning component to their embedded argument. For example, the embedded argument is realized as that-clause in (1) and (2), but un- derstand in (1) marks it as factual and hope in (2) as uncertain. The verb pretend in (3) realizes its embedded argument as non-finite construction and marks it as non-factual. (1) He understands that his computer has a hardware problem. (2) She hopes that her experience will help others. (3) He pretends to take notes on his laptop, but really is updating his Facebook profile. The entities expressed by embedded clausal and non-finite arguments are also called “abstract ob- ject” (AO) in the rest of this paper (follow- ing Asher (1993)); we will use the linguistic term “modality” (Hacquard, 2011) to subsume the meanings (such as factuality, non-factuality and uncertainty) denoted by AO-selecting verbs. As AO-selecting verbs can change the mean- ing of a text in important ways, text understand- ing systems should be sensitive to them. In par- ticular, classifications of AO-selecting verbs ac- cording to semantic criteria are important knowl- edge sources for a wide range of NLP applications, such as event tagging (Saur´ı et al., 2005), com- mited belief tagging (Prabhakaran et al., 2010), reported speech tagging (Krestel et al., 2008), the detection of uncertainty (Szarvas et al., 2012) and future-oriented content (Eckle-Kohler et al., 2008), textual entailment (Saur´ı and Pustejovsky, 2007; Lotan et al., 2013), or determining the degree of factuality of a given text (Saur´ı and Pustejovsky, 2012; de Marneffe et al., 2012). Accordingly, various semantic classifications of AO-selecting verbs have been developed, e.g., (Kiparsky and Kiparsky, 1970; Karttunen, 1971; Karttunen, 2012), some of them explicitly in the context of NLP (Nairn et al., 2006; Saur´ı, 2008). However, these classifications are constructed manually and often quite limited in coverage. Consequently, extending or adapting them to spe- cific domains or other languages is a major issue. We propose to address this issue by exploiting the relationship between …

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