Toward a Relation Hierarchy for Information Retrieval

Natural language text can be seen as a symbolic representation of a cognitive state of the creator that comprises concepts and the relations among those concepts. Much work has been done in Information Science, especially within Information Retrieval (IR), concerning the handling of concepts, most notably in the form of keywords. Much less effort has been spent toward the understanding and handling of the semantic relations that contextually bind concepts together. While it has been shown (Wang, et al., 1985) that the use of these semantic relations for query enhancement can increase retrieval effectiveness, the proper handling of semantic relations has a much wider application than just query enhancement. Once relations inherent in text are identified and captured, they can be used to provide contextual information to the concepts in the representations of the text, which otherwise would be treated as if they were independent and separate

DR-LINK: A System Update for TREC-2

this document set, in fact, contained 92% of the judged-relevant documents. The advantage of the cut-off criterion is it's sensitivity to the varied distributions of SFC similarity values for individual Topic Statements, which appears to reflect how "appropriate" a Topic Statement is for a particular database. For many queries, a relatively small portion of the database, when ranked by similarity to the Topic Statement, will need to be further processed. For example, for Topic Statement forty-two, when the goal is 100% recall, the regression formula predicts a cut-off criterion similarity value which requires that only 13% of the ranked output be further processed, and the available relevance judgments show that this pool of documents contains 99% of the documents judged relevant for that query. 2. C. V-8 Matching Given the complete modularity of the first four modules in the system, for the twenty-four month TIPSTER testing, we reordered two modules so that Text Structuring is done prior to Subject Field Coding. This allowed us to implement and test a new version of matching which combines in a unique way the Text Structurer and the Subject Field Coder. We refer to this version as the V-8 model, since eight SFC vectors are produced for each document, one for each of the seven meta-categories, plus one for all of the categories combined. The V-8 model, therefore, provides multiple SFC vectors for each document, thereby representing the distribution of SFCs over the various meta-text components that occur in a news-text document. This means, in the V-8 matching, that if certain content areas of the Topic Statement are required to occur in a document in one meta-text component, e.g. CONSEQUENCE, and other content is required to occur in another meta-text component, e.g. F..

Towards an intelligent and personalized retrieval system

ABSTRACT. Development of an information retrieval system that can be personal-ized to each user requires maintaining and continually updating an interest profile for each individual user. Since people tend to be poor at self-description, it is suggested that profile development and maintenance is an area in which machine learning and knowledge base techniques can be profitably employed. This paper presents a model for such an application of AI techniques

DR-LINK System: Phase I Summary 1. Description of System 1.1 Approach

that retrieval must be at the conceptual level, not the word level. That is, a successful retrieval system must retrieve on the basis of what people mean in their query, not just what they say in their query. The same is true of documents- their representation needs to capture the content at the conceptual level of expression. To accomplish this human-like goal, DR-LINK aims to represent and match documents and queries at all of the available levels of linguistic expression at which meaning is conveyed. Accordingly, we have developed a modular system which processes, represents, and matches text at the lexical, syntactic, semantic, and discourse levels of language. In concert, these levels of representation permit DR-LINK to achieve a level of intelligent retrieval beyond more traditional approaches. The DR-LINK system takes an innovative approach to dealing with the specific characteristics of the information retrieval tasks required in TIPSTER, focusing on the development of a retrieval system where documents as well as queries are enriched with multiple levels of annotation, with the final representation being a network of concepts and relations expressed in a conceptual graph (Sowa, 1984), thereby enabling retrieval based on conceptual relations. Relations are extracted and represented throughout the system at many levels, ranging from relations between words, to case relations between arguments of a verb, to discourse level relations involving whole sections of text. The system's conceptual processing was particularly motivated by various semantic restrictions often found in the TIPSTER topic statements. A retrieval system needs to be able to process natural language sentences and extract key concepts and the implicit relations among them, which cannot be expressed as a set o