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University of Surrey Participation in TREC8: Weirdness Indexing for Logical Document Extrapolation and Retrieval (WILDER)

This paper describes the development of a prototype document retrieval system based on frequency calculations and corpora comparison techniques. The prototype, WILDER, generated simple frequency information based on which calculations of document relevance could be made. The prototype was built to allow the University of Surrey to debut in the U.S. Text Retrieval Competition (TREC). User queries as specified by the TREC organisers were converted into simple word-frequency lists and compared against values for the entire corpus. These relative frequency values indicatively produced document relevance. The application of morphological and empirical heuristics enabled WILDER to produce the ranked frequency lists required. Introduction The ad hoc task of TREC8 investigates the performance of systems in ranking a static set of documents against novel topics (queries). For each topic, the top 1000 documents satisfying the topic are submitted. Recall and precision techniques are used on these rankings to determine the results of the competition overall. We have used term identification and extraction techniques for identifying topics discussed in a given text. In this note we focus on the use of single word terms for identifying topics. The techniques are based on differences between general language texts, texts used in an everyday context, and special language texts. The special language texts are texts written, for instance, by scientists, engineers, business persons and hobbyists in their respective languages of physics, chemistry, engineering, business, and hobbies. English-speaking physicists will use the English rendering of terms of physics and use their knowledge of English language, which they share with other speakers of English. Similarly a Chinese speaking physicist writing in Chinese will use the Chinese rendering of terms plus their knowledge of Chinese which they share with other Chinese speakers. The special language texts can be distinguished from a collection of general language texts at different linguistic levels including lexical, morphological, syntactic and semantic. These differences can be measured quantitatively and qualitatively. Quantitative measures at the lexical level include frequency of usage of single and compound terms in special language texts and their equivalents in general language texts. Morphological differences can also be measured quantitatively by looking at the differences in the inflectional and derivational variants of terms; specialist texts comprise a larger number of plurals than used in general language; specialists use nominalised verbs more extensively than in general language. The key difference at the lexical level, between specialist and general language texts, is in the distribution of the so-called open class words, typically nouns and adjectives, and the closed class words, typically determiners, conjunctions, prepositions and modal verbs. Consider the 100 million-word British