Many theorists have dismissed a priori the idea that distributional information could play a significant role in syntactic category acquisition. We demonstrate empirically that such information provides a powerful cue to syntactic category membership, which can be exploited by a variety of simple, psychologically plausible mechanisms. We present a range of results using a large corpus of child-directed speech and explore their psychological implications. While our results show that a considerable amount of information concerning the syntac-tic categories can be obtained from distributional information alone, we stress that many other sources of information may also be potential contributors to the identification of syntactic classes.

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This paper provides a tutorial introduction to computational studies of how children learn their native languages. Its aim is to make recent advances accessible to the broader research community. and to place them in the context of current theoretical issues. The first section locates computational studies and behavioral studies within a common theoretical framework. The next two sections review two papers that appear in this volume: one on learning the meanings of words and one on learning the sounds of words. The following section highlights an idea which emerges independently in these two papers and which I have dubbed autonomous bootstrapping. Classical bootstrapping hypotheses propose that children begin to get a toe-hold in a particular linguistic domain, such as syntax, by exploiting information from another domain, such as semantics. Autonomous bootstrapping complements the cross-domain acquisition strategies of classical bootstrapping with strategies that apply within a single domain. Autonomous bootstrapping strategies work by representing partial and/or uncertain linguistic knowledge and using it to analyze the input. The next two sections review two more more contributions to this special issue: one on learning word meanings via selectional preferences and one on algorithms for setting grammatical parameters. The final section suggests directions for future research.

Most natural language processing tasks require lexical semantic information. Automated acquisition of this information would thus increase the robustness and portability of NLP systems. This paper describes an acquisition method which makes use of fixed correspondences between derivational affixes and lexical semantic information. One advantage of this method, and of other methods that rely only on surface characteristics of language, is that the necessary input is currently available.

This paper describes a system, WOLIm (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with representations of their meaning. The lexicon learned consists of words paired with meaning representations. WOLFIE is part of an integrated system that learns to parse novel sentences into semantic representations, such as logical database queries. Experimental results are presented demonstrating WOLFIE'S ability to learn useful lexicons for a database interface in four different natural lan- guages. The lexicons learned by WOLFIE are compared to those acquired by a comparable system developed by Siskind (1996).

Introduction The acquisition of word meaning has been extensively studied for the last thirty years in the field of language acquisition. However, the question of how children acquire word meaning remains highly controversial today. Recently, a number of computational studies have examined the emergence of lexical representations in connectionist networks or similar statistical systems, suggesting that word meaning can be acquired by the computation of statistical regularities inherent in the input data. In particular, Elman (1990, 1998) showed that categories of nouns and verbs, and subcategories of animates versus inanimates (within nouns), and transitives versus intransitives (within verbs), can emerge from the network's computing of the lexical co-occurrence properties in the input. Redington, Chater, and Finch (1998) also demonstrated that the use of distributional properties in large-scale speech corpus allows a statistical system to acquire syntactic categories. These s

this paper we will demonstrate that a formalization of its content reveals very little substance in its claims. We state and discuss six conditions that encapsulate the claims of X-bar theory:

How could children possibly acquire their first subcategorization frames? The hypothesis that they directly observe the syntactic structures of the utterances they hear raises two questions. First, how can children parse input utterances reliably without already knowing the syntactic properties of the all words in them? Second, how do children survive the ungrammatical or misconstrued utterances they inevitably encounter? This paper suggests a specific inference algorithm that substantially reduces the effects of ungrammatical or misconstrued input. Since children must have some such inference procedure, they could use approximate cues to determine syntactic structure. In particular, they can use string-local surface cues rather than global constraints. Such cues make it possible to discover relevant syntactic structure in an utterance without already knowing all the words in it. This paper also suggests a possible set of cues for English subcategorization frames that assumes only the ...

Given the difficulties in learning meanings of words by observing the referent, it has been suggested that children use the syntactic context of the word to predict part of its meaning, a hypothesis known as syntactic bootstrapping. Semantic bootstrapping is the opposite theory that the knowledge of semantics helps in acquiring syntax. While there is evidence that children can apply their knowledge of correlations between syntax and semantics to perform bootstrapping, it is not clear how they come to know about these correlations in the first place. Here, a connectionist network is presented that learns to comprehend a miniature language by associating sentences with the corresponding scenes. In doing so, it learns the syntactic/semantic correlations and exhibits bootstrapping behavior. It is argued that such specialized phenomena can emerge when general mechanisms are applied to a specific task, and it is not always necessary to endow the learner with pre-existing specialized mechanisms.

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