Modeling Term Associations for Ad-Hoc Retrieval Performance Within Language Modeling Framework
Previous research has shown that using term associations could improve the effectiveness of information retrieval (IR) systems. However, most of the existing approaches focus on query reformulation. Document reformulation has just begun to be studied recently. In this paper, we study how to utilize term association measures to do document modeling, and what types of measures are effective in document language models. We propose a probabilistic term association measure, compare it to some traditional methods, such as the similarity co-efficient and window-based methods, in the language modeling (LM) framework, and show that significant improvements over query likelihood (QL) retrieval can be obtained. We also compare the method with state-of-the-art document modeling techniques based on latent mixture models.

title: Relevance feedback and inference networks
abstract: Relevance feedback, which modifies queries using judgements of the relevance of a few, highly-ranked documents, has historically been an important method for increasing the performance of information retrieval systems. In this paper, we extend the inference network model introduced by Turtle and Croft to include relevance feedback techniques. The difference between relevance feedback on text abstracts and full text collections is studied. Preliminary results for relevance feedback on the structured queries supported by the inference net model are also reported.

title: Lexical ambiguity and information retrieval
abstract: Lexical ambiguity is a pervasive problem in natural language processing. However, little quantitative information is available about the extent of the problem or about the impact that it has on information retrieval systems. We report on an analysis of lexical ambiguity in information retrieval test collections and on experiments to determine the utility of word meanings for separating relevant from nonrelevant documents. The experiments show that there is considerable ambiguity even in a specialized database. Word senses provide a significant separation between relevant and nonrelevant documents, but several factors contribute to determining whether disambiguation will make an improvement in performance. For example, resolving lexical ambiguity was found to have little impact on retrieval effectiveness for documents that have many words in common with the query. Other uses of word sense disambiguation in an information retrieval context are discussed.

title: Finding similar questions in large question and answer archives
abstract: There has recently been a significant increase in the number of community-based question and answer services on the Web where people answer other peoples' questions. These services rapidly build up large archives of questions and answers, and these archives are a valuable linguistic resource. One of the major tasks in a question and answer service is to find questions in the archive that a semantically similar to a user's question. This enables high quality answers from the archive to be retrieved and removes the time lag associated with a community-based system. In this paper, we discuss methods for question retrieval that are based on using the similarity between answers in the archive to estimate probabilities for a translation-based retrieval model. We show that with this model it is possible to find semantically similar questions with relatively little word overlap.

title: Supporting Full-Text Information Retrieval with a Persistent Object Store
abstract: Full-text information retrieval systems have unusual and challenging data management requirements. Attempts have been made to satisfy these requirements using traditional (e.g., relational) database management systems. Those attempts, however, have produced rather discouraging results. Instead, information retrieval systems typically use custom data management facilities that require significant development effort and usually do not provide all of the services available from a standard database management system. Advanced data management systems, such as object-oriented database management systems and persistent object stores, offer a reasonable alternative to the two previous approaches. We have taken an existing information retrieval system (INQUERY) and substituted a persistent object store (Mneme) for the portion of the custom data management system that manages an inverted file index. The result is an improvement in performance and significant opportunities for the information retrieval system to take advantage of the standard data management services provided by the persistent object store. We describe our implementation, present performance results on a variety of document collections, and discuss the advantages of using a persistent object store to support information retrieval.

title: Optimization strategies for complex queries
abstract: Previous research into the efficiency of text retrieval systems has dealt primarily with methods that consider inverted lists in sequence; these methods are known as term-at-a-time methods. However, the literature for optimizing document-at-a-time systems remains sparse.We present an improvement to the max_score optimization, which is the most efficient known document-at-a-time scoring method. Like max_score, our technique, called term bounded max_score, is guaranteed to return exactly the same scores and documents as an unoptimized evaluation, which is particularly useful for query model research. We simulated our technique to explore the problem space, then implemented it in Indri, our large scale language modeling search engine. Tests with the GOV2 corpus on title queries show our method to be 23% faster than max_score alone, and 61% faster than our document-at-a-time baseline. Our optimized query times are competitive with conventional term-at-a-time systems on this year's TREC Terabyte task.

title: Ernest Davis, Representations of Commonsense Knowledge

title: Commonsense Metaphysics and Lexical Semantics
abstract: In the TACITUS project for using commonsense knowledge in the understanding of texts about mechanical devices and their failures, we have been developing various commonsense theories that are needed to mediate between the way we talk about the behavior of such devices and causal models of their operation. Of central importance in this effort is the axiomatization of what might be called "commonsense metaphysics". This includes a number of areas that figure in virtually every domain of discourse, such as scalar notions, granularity, time, space, material, physical objects, causality, functionality, force, and shape. Our approach to lexical semantics is then to construct core theories of each of these areas, and then to define, or at least characterize, a large number of lexical items in terms provided by the core theories. In the TACITUS system, processes for solving pragmatics problems posed by a text will use the knowledge base consisting of these theories in conjunction with the logical forms of the sentences in the text to produce an interpretation. In this paper we do not stress these interpretation processes; this is another, important aspect of the TACITUS project, and it will be described in subsequent papers.