Semantic Coherence Facilitates Distributional Learning of Word Meanings

Semantic Coherence Facilitates Distributional Learning of Word Meanings Long Ouyang, Lera Boroditsky, and Michael C. Frank {louyang,lera,mcfrank}@stanford.edu Department of Psychology Stanford University Abstract found that subjects recall “grammatical” MN and PQ sen- tences but also tend to recall ungrammatical MQ and PN sen- tences, suggesting that they learn position regularities (that M/P come first and N/Q come second) but not co-occurrence regularities (that M co-occurs with N but not Q and that P occurs with Q but not N). MNPQ learning has been an im- portant paradigm because it provides an empirical means to consider purely distributional learning of word categories. Braine (1987) investigated the effect of correlating co- occurrence regularities with natural gender. Subjects ac- quired an artificial language by learning to name pictures of referents. In the experimental condition, all pictures of men were labeled by M words and all pictures of women were labeled by P words. Learning of the co-occurrence regular- ities was significantly higher in the experimental condition than in a control condition where natural gender was not pre- dictive of M/P membership. Though Braine’s experiment combined distributional cues with natural gender, he sug- gested that phonological cues might better serve real-world language learners. For instance, Spanish and Italian speak- ers might learn grammatical gender categories by taking ad- vantage of the fact that feminine nouns often end with -a, while masculine nouns often end with -o. Recently, this sug- gestion received attention in the work of Lany and Saffran (2010), who found that 22-month old infants learned MNPQ co-occurrence regularities when they were aligned with the number of syllables in a word – that is, when N words were disyllabic and Q words were monosyllabic, but not when the number of syllables was not predictive of N/Q membership. The defining feature of the artificial language learning paradigm is that, at the outset of the experiment, subjects do not know the meanings of any of the words. Yet, this condi- tion generally does not hold true for real language learners, who typically know the meanings of some words but not oth- ers. It is plausible that the presence of known words facilitates distributional learning. If this is true, how effective are such semantic cues, and how do they compare to (e.g.) phonolog- ical cues? Here, we report the initial results of the first large- scale study comparing the effectiveness of various correlated cues on distributional learning. We presented subjects with an MNPQ language where sentences took the form “M and N” or “P and Q”. We hypothesized that distributional learning would be afforded given a certain level of semantic coher- ence, where all M and P words were familiar and adhered to some semantic organization (e.g., M = animal words, P = vehicle words). For instance, hearing the four sentences “cat and dax”, “cat and ziv”, “car and wug”, and “car and pif” might allow learners to infer that daxes and zivs belong to the Computational work has suggested that one could, in princi- ple, learn aspects of word meaning simply from patterns of co-occurrence between words. The extent to which humans can do this distributional learning is an open question – artifi- cial language learning experiments have yielded mixed results, prompting suggestions that distributional cues must be corre- lated with other cues, such as phonological regularities, for successful learning. We conducted a large-scale experiment comparing how distributional learning is afforded by two dif- ferent types of cues – phonological regularities and semantic coherence. We found that semantic coherence more strongly facilitates distributional learning than onset-consonant phono- logical regularities. Keywords: word learning; distributional learning; semantic coherence Introduction How do people learn what words mean? According to the distributional hypothesis (Harris, 1951; Firth, 1957), patterns of word co-occurrence are a powerful source of information about word meaning. It may be possible to acquire some facets of word meaning by simply keeping track of which linguistic contexts words appear in, even without access to any physical or social cues. Computational models have lent quantitative support for the distributional hypothesis. For ex- ample, the Latent Semantic Analysis model of Landauer & Dumais (1997) clustered words according to their patterns of occurrence across documents and was able to closely match human performance on synonym tests. The success of early models like LSA led to a proliferation of models that use dis- tributional information to learn word meaning (see Riordan & Jones, 2010 for an overview) as well as other linguistic prop- erties such as syntactic category (e.g., Redington, Chater, & Finch, 1998). We know that distributional learning is computationally possible, but the evidence of human capacity for distribu- tional learning is mixed. Some research has found that peo- ple learn co-occurrence statistics and use them to inform cat- egorization (Mintz, 2002; Gerken, Wilson, & Lewis, 2005; Reeder, Newport, & Aslin, 2010), but other work has reached the opposite conclusion (Braine, 1966; Smith, 1966; Frank & Gibson, 2009). A holistic reading of the literature sug- gests that learning is more likely to occur when additional cues are correlated with distributional properties. Below, we review two representative findings using the MNPQ artificial language learning paradigm (Braine, 1966; Smith, 1966). The MNPQ grammar contains four categories of words – M, N, P, and Q – and sentences take one of two forms: MN and PQ. Early investigations (Braine, 1966; Smith, 1966)