Unsupervised grammar induction with simple linguistic constraints
This thesis investigates the problem of unsupervised learning of natural language grammar in the context-free grammar formalism, and argues that linguistic notions are beneficial to the task. Like some recent approaches, this thesis employs distributional clustering, which is based on the linguistic notion of distribution. Although grammar induction is conceptually complicated as it involves both the demarcation between constituents and non-constituents and that between different types of constituents, it is shown in the thesis that these two tasks are actually the two sides of the same coin. That is, nonconstituents can also be classified into different clusters and these clusters are very easy to be separated from those of constituents, and therefore the real problem in grammar induction is how to identify constituents. This thesis provides a generic framework of distributional grammar induction for experimenting with the effect of different criteria for selecting clusters of constituents. Experiments show that a criterion based on the simple principle of minimum variance fails to learn plausible grammars from vast amount of complex data, and it also leads to inconsistency in syntactic analysis as well as
Li, Chi-Ho
Unsupervised grammar induction with simple linguistic constraints. This thesis investigates the problem of unsupervised learning of natural language grammar in the context-free grammar formalism, and argues that linguistic notions are beneficial to the task. Like some recent approaches, this thesis employs distributional clustering, which is based on the linguistic notion of distribution. Although grammar induction is conceptually complicated as it involves both the demarcation between constituents and non-constituents and that between different types of constituents, it is shown in the thesis that these two tasks are actually the two sides of the same coin. That is, nonconstituents can also be classified into different clusters and these clusters are very easy to be separated from those of constituents, and therefore the real problem in grammar induction is how to identify constituents. This thesis provides a generic framework of distributional grammar induction for experimenting with the effect of different criteria for selecting clusters of constituents. Experiments show that a criterion based on the simple principle of minimum variance fails to learn plausible grammars from vast amount of complex data, and it also leads to inconsistency in syntactic analysis as well as
unsupervised grammar induction simple linguistic_constraint investigates problem unsupervised learn natural_language grammar context_free grammar formalism argue linguistic notion beneficial task like recent approach_employ distributional clustering base linguistic notion distribution although grammar induction conceptually complicate involve demarcation constituent non different_type constituent show task actually side coin also classify different cluster cluster easy separate constituent therefore real problem grammar induction identify constituent provide generic framework distributional grammar induction experiment effect_different criterion select cluster constituent show criterion_base simple principle minimum variance fails learn plausible grammar vast_amount complex data also lead inconsistency syntactic_analysis well