Concurrent Acquisition of Word Meaning and Lexical Categories

Learning the meaning of words from ambiguous and noisy context is a challenging task for language learners. It has been suggested that children draw on syntactic cues such as lexical categories of words to constrain potential referents of words in a complex scene. Although the acquisition of lexical categories should be interleaved with learning word meanings, it has not previously been modeled in that fashion. In this paper, we investigate the interplay of word learning and category induction by integrating an LDA-based word class learning module with a probabilistic word learning model. Our results show that the incrementally induced word classes significantly improve word learning, and their contribution is comparable to that of manually assigned part of speech categories. 1 Learning the Meaning of Words For young learners of a natural language, mapping each word to its correct meaning is a challenging task. Words are often used as part of an utterance rather than in isolation. The meaning of an utterance must be inferred from among numerous possible interpretations that the (usually complex) surrounding scene offers. In addition, the linguistic and visual context in which words are heard and used is often noisy and highly ambiguous. Particularly, many words in a language are polysemous and have different meanings. Various learning mechanisms have been proposed for word learning. One well-studied mechanism is cross-situational learning, a bottom-up strategy based on statistical co-occurrence of words and referents across situations (Quine 1960, Pinker 1989). Several experimental studies have shown that adults and children are sensitive to cross-situational evidence and use this information for mapping words to objects, actions and properties (Smith and Yu 2007, Monaghan and Mattock 2009). A number of computational models have been developed based on this principle, demonstrating that cross-situational learning is a powerful and efficient mechanism for learning the correct mappings between words and meanings from noisy input (e.g. Siskind 1996, Yu 2005, Fazly et al. 2010). Another potential source of information that can help the learner to constrain the relevant aspects of a scene is the sentential context of a word. It has been suggested that children draw on syntactic cues provided by the linguistic context in order to guide word learning, a hypothesis known as syntactic bootstrapping (Gleitman 1990). There is substantial evidence that children are sensitive to the structural regularities of language from a very young age, and that they use these structural cues to find the referent of a novel word (e.g. Naigles and Hoff-Ginsberg 1995, Gertner et al. 2006). In particular, young children have robust knowledge of some of the abstract lexical categories such as nouns and verbs (e.g. Gelman and Taylor 1984, Kemp et al. 2005). Recent studies have examined the interplay of cross-situational learning and sentence-level learning mechanisms, showing that adult learners of an artificial language can successfully and simultaneously apply cues and constraints from both sources of information when mapping words to their referents (Gillette et al. 1999, Lidz et al. 2010, Koehne and Crocker 2010; 2011). Several computational models have also investigated this interaction by adding manually annotated part-of-speech tags as