Acquiring Multiword Verbs: The Role of Statistical Evidence

Acquiring Multiword Verbs: The Role of Statistical Evidence Afsaneh Fazly, Aida Nematzadeh, and Suzanne Stevenson Department of Computer Science University of Toronto {afsaneh, aida, suzanne}@cs.toronto. edu Abstract In addition to words and grammar, young children learn a large number of multiword sequences that are semantically idiosyn- cratic and have particular syntactic behaviour, e.g., expressions formed from the combination of a verb and a noun, such as take the train and give a kiss. Given the high degree of polysemy of verbs that commonly participate in such constructions, an important question is what cues children use to identify (non- literal) multiword combinations. We provide evidence that cer- tain statistical cues tapping into the properties of non-literal expressions are useful in separating these from literal com- binations. Moreover, our experiments on naturally occurring child-directed data show that these cues are easily extractable from the input children receive. Introduction Traditional theories of grammar distinguish between lexical knowledge (the individual words that a speaker knows) and grammatical knowledge (the rules for combining words into meaningful utterances). However, there is a rich range of lin- guistic phenomena in the less explored area between words and combinatory rules/constraints. For example, a multiword lexeme such as take the train has an idiosyncratic semantics (“use a train as mode of transport”) that suggest its treat- ment as a lexical unit, but also behaves as a syntactic phrase (e.g., took a train, take the fast train, take trains all over Europe). Much research on language has thus focused on a range of multiword lexemes such as idioms, light verb con- structions, and collocations (e.g., Cowie, 1981; Moon, 1998). Psycholinguists have also shown the importance of contin- gent frequency effects among words and syntactic patterns in the learning and processing of language (Nation et al., 2003; Sosa & MacFarlane, 2002). In theories of language acquisition in particular, especially usage-based accounts of language learning (which eschew complex innate linguistic knowledge), the role of multiword constructions has been emphasized (e.g., Goldberg, 1995; Tomasello, 2003). However, computational modelling of lan- guage acquisition has continued to focus on various aspects of word learning (e.g., Li et al., 2007; Regier, 2005; Yu & Smith, 2006), or grammar learning (e.g., Clark, 2001; Sakas & Fodor, 2001), with work on intermediate constructions mostly limited to identifying general properties of verb argu- ment usages (e.g., Alishahi & Stevenson, 2008; Chang, 2004; Dominey & Inui, 2004). Thus there is a gap in the study of child language acqui- sition that has largely left unaddressed questions about the computational mechanisms that underlie how the child learns to identify multiword lexemes (i.e., recognizing that mean- ing is associated with a group of words, rather than single words plus combinatory rules), and how she determines what syntactic manipulations of them are valid. In contrast, there has been significant work in computational linguistics on this very topic, with development of statistical measures for iden- tifying multiword lexemes in a corpus, and for extracting their usage properties (e.g., Evert et al., 2004; Fazly et al., 2007). Our goal here is to explore whether this computational work on multiword lexemes can be extended in a natural way to the domain of child language acquisition, where an informa- tive cognitive model must take into account the two issues of what kind of data the child is exposed to, and what kinds of processing of that data is cognitively plausible for a child. In pursuing these questions, we focus in particular on the acquisition of multiword verbs, such as take the train and give a kiss. These constructions are a rich and productive source of predication which children must master in most languages, doing so at very young ages (Goldberg, 1995). For exam- ple, consider the following conversation from the CHILDES database (Brown, 1973, sarah130a.cha): *MOT: you’re not gonna take any toys down to the beach today you know. *CHI: why? *MOT: we have to take the train. Here, the mother uses the verb take first in its core literal meaning, and then within a multiword lexeme in which take has a non-literal meaning and combines with the particular argument to express the use of a mode of transportation. The child’s further responses within this conversation give no in- dication that she is puzzled by these very different usages of take. Yet they do pose a very significant puzzle for re- searchers: It has been noted that children learn highly fre- quent verbs (such as take) first (Goldberg, 1995), and yet it is precisely these verbs that are also the most polysemous, showing a wide range of metaphorical sense extensions in multiword lexemes, which children must recognize and deal with. Research over the last few years has shown that the dis- tinctions among literal and non-literal verb–argument combi- nations (such as take the toys versus take the train or take a turn) are in principle learnable based on statistics over usages of such expressions (Fazly & Stevenson, 2007; Venkatapathy & Joshi, 2005). However, such work depends on very large amounts of data (from corpora on the order of 100M words) and on sophisticated statistical and grammatical calculations over such data. The goal here is to determine what is learn- able through the means available to a child — that is, on the