Exploring Contextual Influences on Word Meaning via Multiple-Level Similarity Judgments

Exploring Contextual Influences on Word Meaning Via Multiple-Level Similarity Judgments Nicholas Gaylord (nlgaylord@mail.utexas.edu) Department of Linguistics, 1 University Station B5100 Austin, TX 78712 Abstract be readily captured by a dictionary-type approach to mean- ing (Brown, 2010; Erk et al., 2009). However, the contextual properties that influence the interpretation of a word are not yet clear. This paper presents a preliminary investigation into this question. The goal of this work, broadly put, is to identify what sources of contextual information people use in arriving at a specific interpretation of an ambiguous word. I focus on the identification of broadly relevant properties of senten- tial context, that can be used to predict intuitions of semantic similarity across a wide range of word occurrences. Sentential context can be highly complex, including a host of lexical, syntactic, and pragmatic factors. In order to prop- erly begin a detailed study of contextual influences on the perception of word meaning, it is necessary to simplify both the problem domain and the nature of the sentential context itself. The research reported here focuses on the influence of subject nouns on the interpretation of motion verbs, making use of simplified sentences consisting only of an intransitive verb and a subject noun phrase. Stimuli consisted of pairs of sentences containing the same verb, and I obtained seman- tic similarity judgments for those verb occurrences. While this experiment, on the surface, resembles a sentence com- parison task, that is not its primary intention. Rather, it is only through incorporation of contextual information that the meaning of an ambiguous word becomes precise, and this work focuses on comparison of these contextually-induced specific interpretations. I present the results of a Magnitude Estimation (ME) task (Bard, Robertson, & Sorace, 1996) in which participants were presented with pairs of sentences containing the same verb, and were asked to numerically rate the similarity in meaning of the verb occurrences. These judgments were then analyzed in terms of the similarity of the two subject nouns, which were the only piece of context that varied between the two sentences. Four measures of noun similarity were considered: two based on noun animacy, and two more general similarity measures, one conceptual and one distributional. Animacy is a linguistic semantic feature with known ef- fects in natural language (Bresnan, 2001; Aissen, 2003; Lee, 2003; Mak, Vonk, & Schreifers, 2006), with the added advan- tage that it is applicable to all nouns. Any noun is, minimally, either animate or inanimate, but animacy can also be charac- terized as a scalar feature, in keeping with the hierarchy in (1), from Bresnan (2001): It is well-known that many words’ meanings vary depending on the context in which they are used. This phenomenon has proven difficult to model, and recent work (Klepousniotou, Titone, & Romero, 2008; Erk, McCarthy, & Gaylord, 2009; Brown, 2010) indicates that meaning-in-context varies in a manner much more subtle than would be represented by a dictionary, including reliable fine-grained intuitions about the similarity in meaning of two occurrences of a word in different contexts. This raises the question of how to predict these in- tuitions of semantic similarity on the basis of contextual infor- mation. I present the results of a Magnitude Estimation task in which participants judged the similarity in meaning between pairs of verb occurrences. Stimuli consisted of pairs of sen- tences containing a present tense intransitive motion verb and a singular definite NP subject (e.g. “The kid runs” vs. “The rabbit runs”). As the only varying component of sentential context, differences between the subject nouns in these pairs are hypothesized to predict intuitions about verbal semantic similarity. I explore four measures of noun similarity – two based on noun animacy, as well as conceptual and distribu- tional similarity measures. I find that individually, all of these measures are significant predictors of verbal semantic similar- ity judgments, but that the best model of participant judgments combines all four. This is taken as an indication that a proper use of converging sources of evidence enables more accurate, detailed study of the perception of word meaning in context. Keywords: Magnitude Estimation; semantic similarity; lexi- cal ambiguity; animacy Introduction The term lexical ambiguity refers to a single word having more than one possible meaning. While the existence of lexi- cal ambiguity is uncontroversial, its precise nature is difficult to pin down, particularly with regard to just how many dif- ferent meanings a word has, and how that range of possible meanings is structured. One commonly-recognized subtype of lexical ambiguity is polysemy, where the different mean- ings of a word are related to each other, as with “chicken”, which can mean (literally) a type of bird, (via metonymy) the meat of that bird, or (metaphorically) a cowardly person. However, the different senses of a word are often not as dis- tinguishable as those of “chicken”, making accurate semantic generalizations above the level of individual instances more difficult. It has been argued in the theoretical linguistic liter- ature (Apresjan, 1974) that semantic similarity is actually a matter of degrees, a view that is shared by some lexicogra- phers as well (Hanks, 2000). While there is psycholinguis- tic evidence for graded similarity in meaning between words (Miller & Charles, 1991), the similarity in meaning between different occurrences of a single word is underexplored. There is growing evidence that word meaning does subtly vary from one context to the next in a manner that may not Animacy Hierarchy: humans > animals > insects > natural forces > plants, inanimate objects > abstract notions