Finding structure in language
Since the Chomskian revolution, it has become apparent that natural language is richly structured, being naturally represented hierarchically, and requiring complex context sensitive rules to define regularities over these representations. It is widely assumed that the richness of the posited structure has strong nativist implications for mechanisms which might learn natural language, since it seemed unlikely that such structures could be derived directly from the observation of linguistic data (Chomsky 1965). This thesis investigates the hypothesis that simple statistics of a large, noisy unlabelled corpus of natural language can be exploited to discover some of the structure which exists in natural language automatically. The strategy is to initially assume no knowledge of the structures present in natural language, save that they might be found by analysing statistical regularities which pertain between a word and the words which typically surround it in the corpus. To achieve this, various statistical methods are applied to define similarity between statistical distributions, and to infer a structure for a domain given knowledge of the similarities which pertain within it. Using these tools, it is shown
Finch, Steven
Finding structure in language. Since the Chomskian revolution, it has become apparent that natural language is richly structured, being naturally represented hierarchically, and requiring complex context sensitive rules to define regularities over these representations. It is widely assumed that the richness of the posited structure has strong nativist implications for mechanisms which might learn natural language, since it seemed unlikely that such structures could be derived directly from the observation of linguistic data (Chomsky 1965). This thesis investigates the hypothesis that simple statistics of a large, noisy unlabelled corpus of natural language can be exploited to discover some of the structure which exists in natural language automatically. The strategy is to initially assume no knowledge of the structures present in natural language, save that they might be found by analysing statistical regularities which pertain between a word and the words which typically surround it in the corpus. To achieve this, various statistical methods are applied to define similarity between statistical distributions, and to infer a structure for a domain given knowledge of the similarities which pertain within it. Using these tools, it is shown
finding structure language since revolution become_apparent natural_language richly structure naturally represent hierarchically require complex context sensitive rule define regularity representation widely_assume richness posit structure strong nativist implication mechanism might learn natural_language since seem unlikely structure could derive directly observation linguistic data chomsky investigates hypothesis simple statistic large corpus natural_language exploit discover structure exist natural_language automatically strategy initially assume knowledge structure present natural_language save might find analyse statistical regularity pertain word word typically surround corpus achieve various statistical_method apply define similarity statistical distribution infer structure domain give knowledge similarity pertain within using tool show