There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics
| Autor: | Marco Baroni, Germán Kruszewski, Denis Paperno, Raffaella Bernardi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Linguistics and Language
Computer science media_common.quotation_subject Object (grammar) 02 engineering and technology computer.software_genre 050105 experimental psychology Language and Linguistics Negation introduction Negation Artificial Intelligence Similarity (psychology) 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Conversation Negation as failure Simple (philosophy) media_common business.industry 05 social sciences 16. Peace & justice Computer Science Applications distributional semantics 020201 artificial intelligence & image processing Artificial intelligence Distributional semantics business computer Natural language processing |
| Zdroj: | Computational Linguistics. 42:637-660 |
| ISSN: | 1530-9312 0891-2017 |
| DOI: | 10.1162/coli_a_00262 |
| Popis: | Logical negation is a challenge for distributional semantics, because predicates and their negations tend to occur in very similar contexts, and consequently their distributional vectors are very similar. Indeed, it is not even clear what properties a “negated” distributional vector should possess. However, when linguistic negation is considered in its actual discourse usage, it often performs a role that is quite different from straightforward logical negation. If someone states, in the middle of a conversation, that “This is not a dog,” the negation strongly suggests a restricted set of alternative predicates that might hold true of the object being talked about. In particular, other canids and middle-sized mammals are plausible alternatives, birds are less likely, skyscrapers and other large buildings virtually impossible. Conversational negation acts like a graded similarity function, of the sort that distributional semantics might be good at capturing. In this article, we introduce a large data set of alternative plausibility ratings for conversationally negated nominal predicates, and we show that simple similarity in distributional semantic space provides an excellent fit to subject data. On the one hand, this fills a gap in the literature on conversational negation, proposing distributional semantics as the right tool to make explicit predictions about potential alternatives of negated predicates. On the other hand, the results suggest that negation, when addressed from a broader pragmatic perspective, far from being a nuisance, is an ideal application domain for distributional semantic methods. Jacopo Romoli and Roberto Zamparelli first made us aware of the possible link between alternative semantics and DS. We got the inspiration to work on whether DS can capture alternatives under conversational negation from an informal talk given by Mark Steedman at the 2013 Dagstuhl Seminar on Computational Models of Language Meaning in Context, and from some remarks made by Hans Kamp in the same occasion. Hinrich Schütze first introduced the idea of dynamic DS at the same seminar. We thank Uri Hasson and Raquel Fernandez for important bibliographic advice. We had many illuminating discussions on related topics with Roberto Zamparelli, Gemma Boleda, Nick Asher, the Rovereto Composers, and the members of the intercontinental FLOSS reading group. Finally, we thank the reviewers for helpful comments. Our work is funded by ERC 2011 Starting Independent Research Grant no. 283554 (COMPOSES). |
| Databáze: | OpenAIRE |
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