Dyadic data refers to a domain with two finite sets of objects in which observations are made for dyads, i.e., pairs with one element from either set. This includes event co-occurrences, histogram data, and single stimulus preference data as special cases. Dyadic data arises naturally in many applications ranging from computational linguistics and information retrieval to preference analysis and computer vision. In this paper, we present a systematic, domain-independent framework for unsupervised learning from dyadic data by statistical mixture models. Our approach covers different models with flat and hierarchical latent class structures and unifies probabilistic modeling and structure discovery. Mixture models provide both, a parsimonious yet flexible parameterization of probability distributions with good generalization performance on sparse data, as well as structural information about data-inherent grouping structure. We propose an annealed version of the standard Expectation Maximization algorithm for model fitting which is empirically evaluated on a variety of data sets from different domains.

Clustering is increasing in importance, but linear- and even constant-time clustering algorithms are often too slow for real-time applications. A simple way to speed up clustering is to speed up the distance calculations at the heart of clustering routines. We study two techniques for improving the cost of distance calculations, LSI and truncation, and determine both how much these techniques speed up clustering and how much they affect the quality of the resulting clusters. We find that the speed increase is significant while --- surprisingly --- the quality of clustering is not adversely affected. We conclude that truncation yields clusters as good as those produced by full-profile clustering while offering a significant speed advantage.

This paper proposes an algorithm for word sense disambiguation based on a vector representation of word similarity derived from lexical co-occurrence. It differs from standard approaches by allowing for as fine grained distinctions as is warranted by the information at hand, rather than supposing a fixed number of senses per word, and by allowing for more than one sense to be assigned to a given word occur-rance. The algorithm is applied to the standard vectorspace information retrieval model and an evaluation is performed over the Category B TREC-1 corpus (WSJ subcollection). Results show that this sense disambiguation algorithm improves performance by between 7o and 1o on aver-age.

Searching online text collections can be both rewarding and frustrating. While valuable information can be found, typically many irrelevant documents are also retrieved and many relevant ones are missed. Terminology mismatches between the user's query and document contents are a main cause of retrieval failures. Expanding a user's query with related words can improve search performance, but finding and using related words is an open problem. This research uses corpus analysis techniques to automatically discover similar words directly from the contents of the untagged databases. Using these similarities, user queries are automatically expanded, resulting in conceptual retrieval rather than requiring exact word matches between queries and documents. This work has been extended to multi-database collections where each sub-database has a collection-specific similarity matrix associated with it. If the best matrix is selected, substantial search improvements are possible. However, automati...

As was discussed in section 22, language is central to a correct understanding of the mind. Compositional analytic models perform well in the domain and subject area they are developed for, but any extension is difficult and the models have incomplete psychological veracity. Here we explore how to compute representations of meaning based on a lower level of abstraction and how to use the models for tasks that require some form of language understanding.

Automatic query expansion has been known to be the most important method in overcoming the word mismatch problem in information retrieval. Thesauri have long been used by many researchers as a tool for query expansion. However only one type of thesaurus has generally been used. In this paper we analyze the characteristics of different thesaurus types and propose a method to combine them for query expansion. Experiments using the TREC collection proved the effectiveness of our method over those using one type of thesaurus.

This paper explains a keyword extraction algorithm based solely on a single document. First, frequent terms are extracted. Co-occurrences of a term and frequent terms are counted. If a term appears frequently with a particular subset of terms, the term is likely to have important meaning. The degree of bias of the cooccurrence distribution is measured by the # -measure. We show that our keyword extraction performs well without the need for a corpus. In this paper, a term is defined as a word or a word sequence. We do not intend to limit the meaning in a terminological sense. A word sequence is written as a phrase

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ueries using all search facets identified from requests, low complexity was achieved by formulating queries with major facets only. Query expansion was based on a thesaurus, from which the expansion keys were elicited for queries. There were five expansion types: (1) the first query version was an unexpanded, original query with one search key for each search concept (original search concepts) elicited from the test thesaurus; (2) the synonyms of the original search keys were added to the original query; (3) search keys representing the narrower concepts of the original search concepts were added to the original query; (4) search keys representing the associative concepts of the original search concepts were added to the original query; (5) all previous expansion keys were cumulatively added to the original query. Query structure refers to the syntactic structure of a query expression, marked with query operators and parentheses. The structure of queries was either weak (queries with n

Information Retrieval (IR) is concerned with locating documents that are relevant for a user's information need or query from a large collection of documents. A fundamental problem for information retrieval is word mismatch. A query is usually a short and incomplete description of the underlying information need. The users of IR systems and the authors of the documents often use different words to refer to the same concepts. This thesis addresses the word mismatch problem through automatic text analysis. We investigate two text analysis techniques, corpus analysis and local context analysis, and apply them in two domains of word mismatch, stemming and general query expansion. Experimental results show that these techniques ca...