The Community for Technology Leaders
Green Image
<p><b>Abstract</b>—This paper describes a methodology for semiautomatic grammar induction from unannotated corpora of information-seeking queries in a restricted domain. The grammar contains both semantic and syntactic structures, which are conducive to (spoken) natural language understanding. Our work aims to ameliorate the reliance of grammar development on expert handcrafting or on the availability of annotated corpora. To strive for reasonable coverage on real data, as well as portability across domains and languages, we adopt a statistical approach. Agglomerative clustering using the symmetrized divergence criterion groups words “spatially.” These words have similar left and right contexts and tend to form semantic classes. Agglomerative clustering using mutual information groups words “temporally.” These words tend to co-occur sequentially to form phrases or multiword entities. Our approach is amenable to the optional injection of prior knowledge to catalyze grammar induction. The resultant grammar is interpretable by humans and is amenable to hand-editing for refinement. Hence, our approach is semiautomatic in nature. Experiments were conducted using the <scp>atis</scp> (Air Travel Information Service) corpus and the semiautomatically-induced grammar <tmath>$G_{SA}$</tmath> is compared to an entirely handcrafted grammar <tmath>$G_H$</tmath>. <tmath>$G_H$</tmath> took two months to develop and gave concept error rates of 7 percent and 11.3 percent, respectively, in language understanding of two test corpora. <tmath>$G_{SA}$</tmath> took only three days to produce and gave concept errors of 14 percent and 12.2 percent on the corresponding test corpora. These results provide a desirable trade-off between language understanding performance and grammar development effort.</p>
grammar induction, semantic processing, natural language understanding, concepts extraction, knowledge acquisition

H. Meng and K. Siu, "Semiautomatic Acquisition of Semantic Structures for Understanding Domain-Specific Natural Language Queries," in IEEE Transactions on Knowledge & Data Engineering, vol. 14, no. , pp. 172-181, 2002.
99 ms
(Ver 3.3 (11022016))