Three Generative, Lexicalised Models for Statistical Parsing

In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free grammar. We then extend the model to include a probabilistic treatment of both subcategorisation and wh-movement. Results on Wall Street Journal text show that the parser performs at 88.1/87.5% constituent precision/recall, an average improvement of 2.3% over (Collins 96).Comment: 8 pages, to appear in Proceedings of ACL/EACL 97

A Structured SVM Semantic Parser Augmented by Semantic Tagging with Conditional Random Field

PACLIC 19 / Taipei, taiwan / December 1-3, 200

FANDA: A Novel Approach to Perform Follow-up Query Analysis

Recent work on Natural Language Interfaces to Databases (NLIDB) has attracted considerable attention. NLIDB allow users to search databases using natural language instead of SQL-like query languages. While saving the users from having to learn query languages, multi-turn interaction with NLIDB usually involves multiple queries where contextual information is vital to understand the users' query intents. In this paper, we address a typical contextual understanding problem, termed as follow-up query analysis. In spite of its ubiquity, follow-up query analysis has not been well studied due to two primary obstacles: the multifarious nature of follow-up query scenarios and the lack of high-quality datasets. Our work summarizes typical follow-up query scenarios and provides a new FollowUp dataset with $1000$ query triples on 120 tables. Moreover, we propose a novel approach FANDA, which takes into account the structures of queries and employs a ranking model with weakly supervised max-margin learning. The experimental results on FollowUp demonstrate the superiority of FANDA over multiple baselines across multiple metrics.Comment: Accepted by AAAI 201

Extending a set-theoretic implementation of Montague Semantics to accommodate n-ary transitive verbs.

Natural-language querying of databases remains an important and challenging area. Many approaches have been proposed over many years yet none of them has provided a comprehensive fully-compositional denotational semantics for a large sub-set of natural language, even for querying first-order non-intentional, non-modal, relational databases. One approach, which has made significant progress, is that which is based on Montague Semantics. Various researchers have helped to develop this approach and have demonstrated its viability. However, none have yet shown how to accommodate transitive verbs of arity greater than two. Our thesis is that existing approaches to the implementation of Montague Semantics in modern functional programming languages can be extended to solve this problem. This thesis is proven through the development of a compositional semantics for n-ary transitive verbs (n ≥ 2) and implementation in the Miranda programming environment. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .R69. Source: Masters Abstracts International, Volume: 44-03, page: 1413. Thesis (M.Sc.)--University of Windsor (Canada), 2005