Query understanding enhanced by hierarchical parsing structures

Query understanding has been well studied in the areas of information retrieval and spoken language understanding (SLU). There are generally three layers of query understanding: domain classification, user intent detection, and semantic tagging. Classifiers can be applied to domain and intent detection in real systems, and semantic tagging (or slot filling) is commonly defined as a sequence-labeling task-- mapping a sequence of words to a sequence of labels. Various statistical features (e.g., n-grams) can be extracted from annotated queries for learning label prediction models; however, linguistic characteristics of queries, such as hierarchical structures and semantic relationships, are usually neglected in the feature extraction process. In this work, we propose an approach that leverages linguistic knowledge encoded in hierarchical parse trees for query understanding. Specifically, for natural language queries, we extract a set of syntactic structural features and semantic dependency features from query parse trees to enhance inference model learning. Experiments on real natural language queries show that augmenting sequence labeling models with linguistic knowledge can improve query understanding performance in various domains. Index Terms — query understanding, semantic tagging, linguistic parsin

Harvesting and summarizing user-generated content for advanced speech-based human-computer interaction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-164).There have been many assistant applications on mobile devices, which could help people obtain rich Web content such as user-generated data (e.g., reviews, posts, blogs, and tweets). However, online communities and social networks are expanding rapidly and it is impossible for people to browse and digest all the information via simple search interface. To help users obtain information more efficiently, both the interface for data access and the information representation need to be improved. An intuitive and personalized interface, such as a dialogue system, could be an ideal assistant, which engages a user in a continuous dialogue to garner the user's interest and capture the user's intent, and assists the user via speech-navigated interactions. In addition, there is a great need for a type of application that can harvest data from the Web, summarize the information in a concise manner, and present it in an aggregated yet natural way such as direct human dialogue. This thesis, therefore, aims to conduct research on a universal framework for developing speech-based interface that can aggregate user-generated Web content and present the summarized information via speech-based human-computer interaction. To accomplish this goal, several challenges must be met. Firstly, how to interpret users' intention from their spoken input correctly? Secondly, how to interpret the semantics and sentiment of user-generated data and aggregate them into structured yet concise summaries? Lastly, how to develop a dialogue modeling mechanism to handle discourse and present the highlighted information via natural language? This thesis explores plausible approaches to tackle these challenges. We will explore a lexicon modeling approach for semantic tagging to improve spoken language understanding and query interpretation. We will investigate a parse-and-paraphrase paradigm and a sentiment scoring mechanism for information extraction from unstructured user-generated data. We will also explore sentiment-involved dialogue modeling and corpus-based language generation approaches for dialogue and discourse. Multilingual prototype systems in multiple domains have been implemented for demonstration.by Jingjing Liu.Ph.D

A Semi-Supervised Approach to the Construction of Semantic Lexicons

A growing number of applications require dictionaries of words belonging to semantic classes present in specialized domains. Manually constructed knowledge bases often do not provide sufficient coverage of specialized vocabulary and require substantial effort to build and keep up-to-date. In this thesis, we propose a semi-supervised approach to the construction of domain-specific semantic lexicons based on the distributional similarity hypothesis. Our method starts with a small set of seed words representing the target class and an unannotated text corpus. It locates instances of seed words in the text and generates lexical patterns from their contexts; these patterns in turn extract more words/phrases that belong to the semantic category in an iterative manner. This bootstrapping process can be continued until the output lexicon reaches the desired size. We explore employing techniques such as learning lexicons for multiple semantic classes at the same time and using feedback from competing lexicons to increase the learning precision. Evaluated for extraction of dish names and subjective adjectives from a corpus of restaurant reviews, our approach demonstrates great flexibility in learning various word classes, and also performance improvements over state of the art bootstrapping and distributional similarity techniques for the extraction of semantically similar words. Its shallow lexical patterns also prove to perform superior to syntactic patterns in capturing the semantic class of words