Improved cross-language information retrieval via disambiguation and vocabulary discovery

Cross-lingual information retrieval (CLIR) allows people to find documents irrespective of the language used in the query or document. This thesis is concerned with the development of techniques to improve the effectiveness of Chinese-English CLIR. In Chinese-English CLIR, the accuracy of dictionary-based query translation is limited by two major factors: translation ambiguity and the presence of out-of-vocabulary (OOV) terms. We explore alternative methods for translation disambiguation, and demonstrate new techniques based on a Markov model and the use of web documents as a corpus to provide context for disambiguation. This simple disambiguation technique has proved to be extremely robust and successful. Queries that seek topical information typically contain OOV terms that may not be found in a translation dictionary, leading to inappropriate translations and consequent poor retrieval performance. Our novel OOV term translation method is based on the Chinese authorial practice of including unfamiliar English terms in both languages. It automatically extracts correct translations from the web and can be applied to both Chinese-English and English-Chinese CLIR. Our OOV translation technique does not rely on prior segmentation and is thus free from seg mentation error. It leads to a significant improvement in CLIR effectiveness and can also be used to improve Chinese segmentation accuracy. Good quality translation resources, especially bilingual dictionaries, are valuable resources for effective CLIR. We developed a system to facilitate construction of a large-scale translation lexicon of Chinese-English OOV terms using the web. Experimental results show that this method is reliable and of practical use in query translation. In addition, parallel corpora provide a rich source of translation information. We have also developed a system that uses multiple features to identify parallel texts via a k-nearest-neighbour classifier, to automatically collect high quality parallel Chinese-English corpora from the web. These two automatic web mining systems are highly reliable and easy to deploy. In this research, we provided new ways to acquire linguistic resources using multilingual content on the web. These linguistic resources not only improve the efficiency and effectiveness of Chinese-English cross-language web retrieval; but also have wider applications than CLIR

Mixed-Language Arabic- English Information Retrieval

Includes abstract.Includes bibliographical references.This thesis attempts to address the problem of mixed querying in CLIR. It proposes mixed-language (language-aware) approaches in which mixed queries are used to retrieve most relevant documents, regardless of their languages. To achieve this goal, however, it is essential firstly to suppress the impact of most problems that are caused by the mixed-language feature in both queries and documents and which result in biasing the final ranked list. Therefore, a cross-lingual re-weighting model was developed. In this cross-lingual model, term frequency, document frequency and document length components in mixed queries are estimated and adjusted, regardless of languages, while at the same time the model considers the unique mixed-language features in queries and documents, such as co-occurring terms in two different languages. Furthermore, in mixed queries, non-technical terms (mostly those in non-English language) would likely overweight and skew the impact of those technical terms (mostly those in English) due to high document frequencies (and thus low weights) of the latter terms in their corresponding collection (mostly the English collection). Such phenomenon is caused by the dominance of the English language in scientific domains. Accordingly, this thesis also proposes reasonable re-weighted Inverse Document Frequency (IDF) so as to moderate the effect of overweighted terms in mixed queries

Effective web crawlers

Web crawlers are the component of a search engine that must traverse the Web, gathering documents in a local repository for indexing by a search engine so that they can be ranked by their relevance to user queries. Whenever data is replicated in an autonomously updated environment, there are issues with maintaining up-to-date copies of documents. When documents are retrieved by a crawler and have subsequently been altered on the Web, the effect is an inconsistency in user search results. While the impact depends on the type and volume of change, many existing algorithms do not take the degree of change into consideration, instead using simple measures that consider any change as significant. Furthermore, many crawler evaluation metrics do not consider index freshness or the amount of impact that crawling algorithms have on user results. Most of the existing work makes assumptions about the change rate of documents on the Web, or relies on the availability of a long history of change. Our work investigates approaches to improving index consistency: detecting meaningful change, measuring the impact of a crawl on collection freshness from a user perspective, developing a framework for evaluating crawler performance, determining the effectiveness of stateless crawl ordering schemes, and proposing and evaluating the effectiveness of a dynamic crawl approach. Our work is concerned specifically with cases where there is little or no past change statistics with which predictions can be made. Our work analyses different measures of change and introduces a novel approach to measuring the impact of recrawl schemes on search engine users. Our schemes detect important changes that affect user results. Other well-known and widely used schemes have to retrieve around twice the data to achieve the same effectiveness as our schemes. Furthermore, while many studies have assumed that the Web changes according to a model, our experimental results are based on real web documents. We analyse various stateless crawl ordering schemes that have no past change statistics with which to predict which documents will change, none of which, to our knowledge, has been tested to determine effectiveness in crawling changed documents. We empirically show that the effectiveness of these schemes depends on the topology and dynamics of the domain crawled and that no one static crawl ordering scheme can effectively maintain freshness, motivating our work on dynamic approaches. We present our novel approach to maintaining freshness, which uses the anchor text linking documents to determine the likelihood of a document changing, based on statistics gathered during the current crawl. We show that this scheme is highly effective when combined with existing stateless schemes. When we combine our scheme with PageRank, our approach allows the crawler to improve both freshness and quality of a collection. Our scheme improves freshness regardless of which stateless scheme it is used in conjunction with, since it uses both positive and negative reinforcement to determine which document to retrieve. Finally, we present the design and implementation of Lara, our own distributed crawler, which we used to develop our testbed