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this paper we outline a research program for computational linguistics, making extensive use of text corpora. We demonstrate how a semantic framework for lexical knowledge can suggest richer relationships among words in text beyond that of simple co-occurrence. The work suggests how linguistic phenomena such as metonymy and polysemy might be exploitable for semantic tagging of lexical items. Unlike with purely statistical collocational analyses, the framework of a semantic theory allows the automatic construction of predictions about deeper semantic relationships among words appearing in collocational systems. We illustrate the approach for the acquisition of lexical information for several classes of nominals, and how such techniques can fine-tune the lexical structures acquired from an initial seeding of a machine-readable dictionary. In addition to conventional lexical semantic relations, we show how information concerning lexical presuppositions and preference relations can also be acquired from corpora, when analyzed with the appropriate semantic tools. Finally, we discuss the potential that corpus studies have for enriching the data set for theoretical linguistic research, as well as helping to confirm or disconfirm linguistic hypotheses

Natural language processing systems need large lexicons containing explicit information about lexical-semantlc relationships, selection restrictions, and verb categories. Because the labor involved in constructing such lexicons by hand is overwhelming, we have been trying to construct lexical entries automatically from information available in the machine-readable version of

: This research presents preliminary results generated from the semantic retrieval research component of the Illinois Digital Library Initiative (DLI) project. Using a variation of the automatic thesaurus generation techniques, to which we refer as the concept space approach, we aimed to create graphs of domain-specific concepts (terms) and their weighted co-occurrence relationships for all major engineering domains. Merging these concept spaces and providing traversal paths across different concept spaces could potentially help alleviate the vocabulary (difference) problem evident in large-scale information retrieval. We have experimented previously with such a technique for a smaller molecular biology domain (Worm Community System, with 10+ MBs of document collection) with encouraging results. In order to address the scalability issue related to large-scale information retrieval and analysis for the current Illinois DLI project, we recently conducted experiments using the concept sp...

This thesis aims at building a Lexical Knowledge Base (LKB) that will be useful to a Natural Language Processing (NLP) system by extracting information from a Machine Readable Dictionary (MRD). Our source of knowledge is the American Heritage First Dictionary (AHFD) which contains 1800 entries and is designed for children of age six to eight learning the structure and the basic vocabulary of their language. Using a children's dictionary allows us to restrict our vocabulary, but still work on general knowledge about day to day concepts and actions. Our Lexical Knowledge Base contains information extracted from the AHFD and represented using the Conceptual Graph (CG) formalism. The graph definitions explicitly give the information contained in all the noun and verb definitions from the AHFD. Each sentence of each definition is tagged, parsed and automatically transformed into a conceptual graph. The type hierarchy, extracted automatically from the definitions, groups all the nouns a...

Knowledge structures called (kmcep[ Clustering Knowledge Graphs (CCKGs) are in[ro(luccd along with a process for Omit consrnction fi'om a machine tea.d- able dictiona.ry. (3(KGs contain multiplc concepts inLetrelated through multipie senran[it relations roger.]mr 2)rnting a semantic [lustier represented })y a con-.

Although automated word sense disambiguation has become a popular activity within computational lexicology, evaluation of the accuracy of disambiguation systems is still mostly limited to manual checking by the developer. This paper describes our work in collecting data on the disambiguation behavior of human subjects, with the intention of providing (1) a norm against which dictionary-based systems (and perhaps others) can be evaluated, and (2) a source of psycholinguistic information about previously unobserved aspects of human disambiguation, for the use of both psycholinguists and computational researchers. We also describe two of our most important tools: a questionnaire of ambiguous test words in various contexts, and a hypertext user interface for efficient and powerful collection of data from human subjects.

This dissertation has been submitted in partial ful llment of requirements for

Information retrieval systems that support searching of large textual databases are typically accessed by trained search intermediaries who provide assistance to end users in bridging the gap between the languages of authors and inquirers. We are building a thesaurus in the form of a large semantic network io support interactive query expansion and search by end users. Our lexicon is being built by analyzing and merging data from several large English dictionaries; testing of its value for reuieval is with the SMART and CODER systems.

The task of natural language analysis requires background knowledge in order to understand the meaning of text. Building a knowledge base of this background knowledge necessitates the acquisition of information from different sources, the most practical one being electronic texts that are so abundant. So this task of knowledge acquisition is dependent on the processing of electronic texts, with the processing being dependent on acquired knowledge. The entire process can be viewed as a spiral of increased knowledge acquisition and increased language analysis. We focus on the origin of the spiral, and develop design principles for building a dynamic lexical knowledge base (LKB), concentrating on the LKB's content, representation formalism and structure. A dynamic LKB must be modular, with easy access and comparison mechanisms for evaluating new information, and with modification mechanisms for updating information. We examine the role of the LKB in sentence understanding, which encompasses disambiguation as well as modification of the LKB. Finally, the actual construction process of a particular LKB is presented, and some results are provided that make use of a children's dictionary as a starting point for the spiral.