Jinxi Xu

Quary Expansion Using Local and Global Document Analysis

Automatic query expansion has long been suggested as a technique for dealing with the fundamental issue of word mismatch in information retrieval. A number of approaches to expansion have been studied and, more recently, attention has focused on techniques that analyze the corpus to discover word relationship (global techniques) and those that analyze documents retrieved by the initial query ( local feedback). In this paper, we compare the effectiveness of these approaches and show that, although global analysis haa some advantages, local analysia is generally more effective. We also show that using global analysis techniques.

Improving the effectiveness of information retrieval with local context analysis

Techniques for automatic query expansion have been extensively studied in information research as a means of addressing the word mismatch between queries and documents. These techniques can be categorized as either global or local. While global techniques rely on analysis of a whole collection to discover word relationships, local techniques emphasize analysis of the top-ranked documents retrieved for a query. While local techniques have shown to be more effective that global techniques in general, existing local techniques are not robust and can seriously hurt retrieved when few of the retrieval documents are relevant. We propose a new technique, called local context analysis, which selects expansion terms based on cooccurrence with the query terms within the top-ranked documents. Experiments on a number of collections, both English and non-English, show that local context analysis offers more effective and consistent retrieval results.

Cluster-based language models for distributed retrieval

E ective retrieval in a distributed environment is an important but di cult problem. Lack of e ectiveness appears to have three causes. First, collection selection based on word histograms is not appropriate for heterogeneous collections. Second, relevant documents are scattered over many collections and searching a few collections misses many relevant documents. Third, most existing collection selection metrics lack sound theoretical justi cations and hence may not be well tuned to the problem. We propose a new approach to distributed retrieval based on document clustering and language modeling. Document clustering is used to organize collections around topics. Language modeling is used to properly represent topics and e ectively select the right topics for a query. Based on these ideas, three methods are proposed to suit di erent environments. We show that all three methods improve e ectiveness of distributed retrieval.

Corpus-based stemming using cooccurrence of word variants

Stemming is used in many information retrieval (IR) systems to reduce variant word forms to common roots. It is one of the simplest applications of natural-language processing to IR and is one of the most effective in terms of user acceptance and consistency, though small retrieval improvements. Current stemming techniques do not, however, reflect the language use in specific corpora, and this can lead to occasional serious retrieval failures. We propose a technique for using corpus-based word variant cooccurrence statistics to modify or create a stemmer. The experimental results generated using English newspaper and legal text and Spanish text demonstrate the viability of this technique and its advantages relative to conventional approaches that only employ morphological rules.

Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002

Information retrieval (IR) research has reached a point where it is appropriate to assess progress and to define a research agenda for the next five to ten years. This report summarizes a discussion of IR research challenges that took place at a recent workshop. The attendees of the workshop considered information retrieval research in a range of areas chosen to give broad coverage of topic areas that engage information retrieval researchers. Those areas are retrieval models, cross-lingual retrieval, Web search, user modeling, filtering, topic detection and tracking, classification, summarization, question answering, metasearch, distributed retrieval, multimedia retrieval, information extraction, as well as testbed requirements for future work. The potential use of language modeling techniques in these areas was also discussed. The workshop identified major challenges within each of those areas. The following are recurring themes that ran throughout: • User and context sensitive retrieval • Multi-lingual and multi-media issues • Better target tasks • Improved objective evaluations • Substantially more labeled data • Greater variety of data sources • Improved formal models Contextual retrieval and global information access were identified as particularly important long-term challenges.

Effective retrieval with distributed collections

Abstract : This paper evaluates the retrieval effectiveness of distributed information retrieval systems in realistic environments. We find that when a large number of collections are available, the retrieval effectiveness is significantly worse than that of centralized systems, mainly because typical queries are not adequate for the purpose of choosing the right collections. We propose two techniques to address the problem. One is to use phrase information in the collection selection index and the other is query expansion. Both techniques enhance the discriminatory power of typical queries for choosing the right collections and hence significantly improve retrieval results. Query expansion, in particular, brings the effectiveness of searching a large set of distributed collections close to that of searching a centralized collection.

Evaluating a probabilistic model for cross-lingual information retrieval

This work proposes and evaluates a probabilistic cross-lingual retrieval system. The system uses a generative model to estimate the probability that a document in one language is relevant, given a query in another language. An important component of the model is translation probabilities from terms in documents to terms in a query. Our approach is evaluated when 1) the only resource is a manually generated bilingual word list, 2) the only resource is a parallel corpus, and 3) both resources are combined in a mixture model. The combined resources produce about 90% of monolingual performance in retrieving Chinese documents. For Spanish the system achieves 85% of monolingual performance using only a pseudo-parallel Spanish-English corpus. Retrieval results are comparable with those of the structural query translation technique (Pirkola, 1998) when bilingual lexicons are used for query translation. When parallel texts in addition to conventional lexicons are used, it achieves better retrieval results but requires more computation than the structural query translation technique. It also produces slightly better results than using a machine translation system for CLIR, but the improvement over the MT system is not significant.

Empirical studies in strategies for Arabic retrieval

This work evaluates a few search strategies for Arabic monolingual and cross-lingual retrieval, using the TREC Arabic corpus as the test-bed. The release by NIST in 2001 of an Arabic corpus of nearly 400k documents with both monolingual and cross-lingual queries and relevance judgments has been a new enabler for empirical studies. Experimental results show that spelling normalization and stemming can significantly improve Arabic monolingual retrieval. Character tri-grams from stems improved retrieval modestly on the test corpus, but the improvement is not statistically significant. To further improve retrieval, we propose a novel thesaurus-based technique. Different from existing approaches to thesaurus-based retrieval, ours formulates word synonyms as probabilistic term translations that can be automatically derived from a parallel corpus. Retrieval results show that the thesaurus can significantly improve Arabic monolingual retrieval. For cross-lingual retrieval (CLIR), we found that spelling normalization and stemming have little impact.

Effective Use of Linguistic and Contextual Information for Statistical Machine Translation

Current methods of using lexical features in machine translation have difficulty in scaling up to realistic MT tasks due to a prohibitively large number of parameters involved. In this paper, we propose methods of using new linguistic and contextual features that do not suffer from this problem and apply them in a state-of-the-art hierarchical MT system. The features used in this work are non-terminal labels, non-terminal length distribution, source string context and source dependency LM scores. The effectiveness of our techniques is demonstrated by significant improvements over a strong base-line. On Arabic-to-English translation, improvements in lower-cased BLEU are 2.0 on NIST MT06 and 1.7 on MT08 newswire data on decoding output. On Chinese-to-English translation, the improvements are 1.0 on MT06 and 0.8 on MT08 newswire data.

String-to-dependency statistical machine translation

We propose a novel string-to-dependency algorithm for statistical machine translation. This algorithm employs a target dependency language model during decoding to exploit long distance word relations, which cannot be modeled with a traditional n-gram language model. Experiments show that the algorithm achieves significant improvement in MT performance over a state-of-the-art hierarchical string-to-string system on NIST MT06 and MT08 newswire evaluation sets.