Peter Anick

Exploiting clustering and phrases for context-based information retrieval

This paper explores exploiting the synergy between document clustering and phrasal analysis for the purpose of automatically constructing a corrrex~-busedretrieval system. A contex~ consists of two components a cluster of logically related articles (its exrension) and a small set of salient concepts, represented by words and phrases and organized by the cluster’s key terms (its irr~ertsion). At inn-time, the system presents contexts that best match the result list of a user’s natural language query. The user can then choose a context and manipulate the intensionsd component to both browse the context’s extension and launch new searches over the entire database. We argue that the focused relevance feedback provided by contexts, at a level of abstraction higher than individual documents and lower than the database as a whole, provides a natural way for users to refine vague information needs and helps to blur the distinction between searching and browsing. The I%zraphrase interface, running over a database of business-related news articles, is used to illustrate the advantages of such a context-based retrieval paradigm.

Adapting a full-text information retrieval system to the computer troubleshooting domain

There has been much research in full-text information retrieval on automated and semi-automated methods of query expansion to improve the effectiveness of user queries. In this paper we consider the challenges of tuning an IR system to the domain of computer troubleshooting, where user queries tend to be very short and natural language query terms are intermixed with terminology from a variety of technical sublanguages. A number of heuristic techniques for domain knowledge acquisition are described in which the complementary contributions of query log data and corpus analysis are exploited. We discuss the implications of sublanguage domain tuning for run-time query expansion tools and document indexing, arguing that the conventional devices for more purely “natural language” domains may be inadequate.

On the semantic interpretation of nominals

In this paper we examine a subset of polysemous elements, the logical structure of nominals, and argue that many cases of polysemy have well-defined calculi, which interact with the grammar in predictable and determinate ways for disambiguation. These calculi constitute part of the lexical organization of the grammar and contribute to the lexical semantics of a word. The lexical system of the grammar is distinct from the conceptual representation associated with a lexical item, where polysemy is less constrained by grammar. We propose a structured semantic representation, the Lexical Conceptual Paradigm (LCP) which groups nouns into paradigmatic classes exhibiting like behavior.

An application of lexical semantics to knowledge acquisition from corpora

In this paper, we describe a program of research designed to explore how a lexical semantic theory may be exploited for extracting information from corpora suitable for use in Information Retrieval applications. Unlike with purely statistical collocational analyses, the framework of a semantic theory allows the automatic construction of predictions about semantic relationships among words appearing in collocational systems. We illustrate the approach for the acquisition of lexical information for several classes of nominals.

Lexical Structures or Linguistic Inference

In order to resolve metonymy and other violations of selectional restrictions between lexical items, a language understander must be able to infer relationships that do not have explicit lexical analogs in the sentance. Although such inferencing has typically been relegated to the world knowledge portion of a natural language processing system, there is also evidence, from both theoretical analysis in compositional semantics and distributional analysis of corpus data, that some cases of metonymy may best be processed with respect to more specific lexical and syntactic constructions. In this paper, we argue how the richer vocabulary for lexical semantics proposed in Pustejovskys “Generative Lexicon” theory allows one to explore the role of lexical information in such cases, and therefore sheds more light on the distinction between lexical inferences, which follow from defaults associated with lexical items and rules of composition, and pragmatic inferences, which depend on reasoning with respect to the context of the utterance.