Construction and Visualization of Key Term Hierarchies

Construction and Visualization of Key Term Hierarchies Joe Zhou and Troy Tanner LEXIS-NEXIS, a Division of Reed Elsevier 9555 Springboro Pike Miamisburg, OH 45342 {joez, tit} @ lexis-nexis.com Abstract This paper presents a prototype system for key term manipulation and visualization in a real-world commercial environment. The system consists of two components. A preprocessor generates a set of key terms from a text dataset which represents a specific topic. The generated key terms are orga- nized in a hierarchical structure and fed into a graphic user interface (GUI). The friendly and inter- active GUI toolkit allows the user to visualize the key terms in context and explore the content of the original dataset. 1. INTRODUCTION As the amount of on-line text grows at an exponen- tial rate, developing useful text analysis techniques and tools to access information content from vari- ous electronic sources is becoming increasingly important. In this paper we present an applied research prototype system that intends to accom- plish two major tasks. First, a set of key terms, ranging from single word terms to four word terms, are automatically generated and organized in a hierarchical structure out of a text dataset which represents a specific topic. Second, a graphic user interface (GUI) is established that provides the domain expert or the user with an interactive envi- ronment to visualize the key term hierarchy in the context of the original dataset. 2. SYSTEM DESCRIPTION The ultimate goal of this prototype system is to offer an automated toolkit which allows the domain expert or the user to visualize and examine key terms in a large information collection. Such a tool- kit has proven to be useful in a number of real applications. For example, it has helped us reduce the time and manual effort needed to develop and maintain our on-line document indexing and classi- fication schemes. The system consists of two components: a prepro- cessing component for the automatic construction of key terms and the front-end component for user- guided graphic interface. 2.1 Automatic Generation of Key Terms Automatically identifying meaningful terms from naturally running texts has been an important task for information technologists. It is widely believed that a set of good terms can be used to express the content of the document. By capturing a set of good terms, for example, relevant documents can be searched and retrieved from a large document collection. Though what constitutes a good term still remains to be answered, we know that a good term can be a word stem, a single word, a multiple word term (a phrase), or simply a syntactic unit. Various existing and workable term extraction tools are either statistically driven, or linguistically oriented, or some hybrid of the two. They all target frequently co-occurring words in running text. The earlier work of Choueka (1988) proposed a pure frequency approach in which only quantitative selection criteria were established and applied. Church and Hanks (1990) introduced a statistical measurement called mutual information for extracting strongly associated or collocated …

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