DBFIRE: recuperação de documentos relacionados a consultas a banco de dados.

Bancos de dados e documentos são comumente mantidos em separado nas organizações, controlados por Sistemas Gerenciadores de Bancos de Dados (SGBDs) e Sistemas de Recuperação de Informação (SRIs), respectivamente. Essa separação tem ligação com a natureza dos dados manipulados: estruturados, no primeiro caso; não estruturados, no segundo. Enquanto os SGBDs processam consultas exatas a bancos de dados, os SRIs recuperam documentos com base em buscas por palavras-chave, que são inerentemente imprecisas. Apesar disso, a integração desses sistemas pode resultar em grandes ganhos ao usuário, uma vez que, numa mesma organização, bancos de dados e documentos frequentemente se referem a entidades comuns. Uma das possibilidades de integração é a recuperação de documentos associados a uma dada consulta a banco de dados. Por exemplo, considerando a consulta "Quais os clientes com contratos acima de X reais?", como recuperar documentos que possam estar associados a esta consulta, como os próprios contratos desses clientes, propostas de novas vendas em aberto, entre outros documentos? A solução proposta nesta tese baseia-se numa abordagem especial de expansão de busca para a recuperação de documentos: um conjunto inicial de palavras-chave é expandido com termos potencialmente úteis contidos no resultado de uma consulta a banco de dados; o conjunto de palavras-chave resultante é então enviado a um SRI para a recuperação dos documentos de interesse para a consulta. Propõe-se ainda uma nova forma de ordenação dos termos para expansão: partindo do pressuposto de que uma consulta a banco de dados representa com exatidão a necessidade de informação do usuário, a seleção dos termos é medida por sua difusão ao longo do resultado da consulta. Essa medida é usada não apenas para selecionar os melhores termos, mas também para estabelecer seus pesos relativos na expansão. Para validar o método proposto, foram realizados experimentos em dois domínios distintos, com resultados evidenciando melhorias significativas em termos da recuperação de documentos relacionados às consultas na comparação com outros modelos destacados na literatura

Learning to select for information retrieval

The effective ranking of documents in search engines is based on various document features, such as the frequency of the query terms in each document, the length, or the authoritativeness of each document. In order to obtain a better retrieval performance, instead of using a single or a few features, there is a growing trend to create a ranking function by applying a learning to rank technique on a large set of features. Learning to rank techniques aim to generate an effective document ranking function by combining a large number of document features. Different ranking functions can be generated by using different learning to rank techniques or on different document feature sets. While the generated ranking function may be uniformly applied to all queries, several studies have shown that different ranking functions favour different queries, and that the retrieval performance can be significantly enhanced if an appropriate ranking function is selected for each individual query. This thesis proposes Learning to Select (LTS), a novel framework that selectively applies an appropriate ranking function on a per-query basis, regardless of the given query's type and the number of candidate ranking functions. In the learning to select framework, the effectiveness of a ranking function for an unseen query is estimated from the available neighbouring training queries. The proposed framework employs a classification technique (e.g. k-nearest neighbour) to identify neighbouring training queries for an unseen query by using a query feature. In particular, a divergence measure (e.g. Jensen-Shannon), which determines the extent to which a document ranking function alters the scores of an initial ranking of documents for a given query, is proposed for use as a query feature. The ranking function which performs the best on the identified training query set is then chosen for the unseen query. The proposed framework is thoroughly evaluated on two different TREC retrieval tasks (namely, Web search and adhoc search tasks) and on two large standard LETOR feature sets, which contain as many as 64 document features, deriving conclusions concerning the key components of LTS, namely the query feature and the identification of neighbouring queries components. Two different types of experiments are conducted. The first one is to select an appropriate ranking function from a number of candidate ranking functions. The second one is to select multiple appropriate document features from a number of candidate document features, for building a ranking function. Experimental results show that our proposed LTS framework is effective in both selecting an appropriate ranking function and selecting multiple appropriate document features, on a per-query basis. In addition, the retrieval performance is further enhanced when increasing the number of candidates, suggesting the robustness of the learning to select framework. This thesis also demonstrates how the LTS framework can be deployed to other search applications. These applications include the selective integration of a query independent feature into a document weighting scheme (e.g. BM25), the selective estimation of the relative importance of different query aspects in a search diversification task (the goal of the task is to retrieve a ranked list of documents that provides a maximum coverage for a given query, while avoiding excessive redundancy), and the selective application of an appropriate resource for expanding and enriching a given query for document search within an enterprise. The effectiveness of the LTS framework is observed across these search applications, and on different collections, including a large scale Web collection that contains over 50 million documents. This suggests the generality of the proposed learning to select framework. The main contributions of this thesis are the introduction of the LTS framework and the proposed use of divergence measures as query features for identifying similar queries. In addition, this thesis draws insights from a large set of experiments, involving four different standard collections, four different search tasks and large document feature sets. This illustrates the effectiveness, robustness and generality of the LTS framework in tackling various retrieval applications

Parallel corpus multi stream question answering with applications to the Qu'ran

Question-Answering (QA) is an important research area, which is concerned with developing an automated process that answers questions posed by humans in a natural language. QA is a shared task for the Information Retrieval (IR), Information Extraction (IE), and Natural Language Processing communities (NLP). A technical review of different QA system models and methodologies reveals that a typical QA system consists of different components to accept a natural language question from a user and deliver its answer(s) back to the user. Existing systems have been usually aimed at structured/ unstructured data collected from everyday English text, i.e. text collected from television programmes, news wires, conversations, novels and other similar genres. Despite all up-to-date research in the subject area, a notable fact is that none of the existing QA Systems has been tested on a Parallel Corpus of religious text with the aim of question answering. Religious text has peculiar characteristics and features which make it more challenging for traditional QA methods than other kinds of text. This thesis proposes PARMS (Parallel Corpus Multi Stream) Methodology; a novel method applying existing advanced IR (Information Retrieval) techniques, and combining them with NLP (Natural Language Processing) methods and additional semantic knowledge to implement QA (Question Answering) for a parallel corpus. A parallel Corpus involves use of multiple forms of the same corpus where each form differs from others in a certain aspect, e.g. translations of a scripture from one language to another by different translators. Additional semantic knowledge can be referred as a stream of information related to a corpus. PARMS uses Multiple Streams of semantic knowledge including a general ontology (WordNet) and domain-specific ontologies (QurTerms, QurAna, QurSim). This additional knowledge has been used in embedded form for Query Expansion, Corpus Enrichment and Answer Ranking. The PARMS Methodology has wider applications. This thesis applies it to the Quran – the core text of Islam; as a first case study. The PARMS Method uses parallel corpus comprising ten different English translations of the Quran. An individual Quranic verse is treated as an answer to questions asked in a natural language, English. This thesis also implements PARMS QA Application as a proof of concept for the PARMS methodology. The PARMS Methodology aims to evaluate the range of semantic knowledge streams separately and in combination; and also to evaluate alternative subsets of the DATA source: QA from one stream vs. parallel corpus. Results show that use of Parallel Corpus and Multiple Streams of semantic knowledge have obvious advantages. To the best of my knowledge, this method is developed for the first time and it is expected to be a benchmark for further research area

Towards effective cross-lingual search of user-generated internet speech

The very rapid growth in user-generated social spoken content on online platforms is creating new challenges for Spoken Content Retrieval (SCR) technologies. There are many potential choices for how to design a robust SCR framework for UGS content, but the current lack of detailed investigation means that there is a lack of understanding of the specifc challenges, and little or no guidance available to inform these choices. This thesis investigates the challenges of effective SCR for UGS content, and proposes novel SCR methods that are designed to cope with the challenges of UGS content. The work presented in this thesis can be divided into three areas of contribution as follows. The first contribution of this work is critiquing the issues and challenges that in influence the effectiveness of searching UGS content in both mono-lingual and cross-lingual settings. The second contribution is to develop an effective Query Expansion (QE) method for UGS. This research reports that, encountered in UGS content, the variation in the length, quality and structure of the relevant documents can harm the effectiveness of QE techniques across different queries. Seeking to address this issue, this work examines the utilisation of Query Performance Prediction (QPP) techniques for improving QE in UGS, and presents a novel framework specifically designed for predicting of the effectiveness of QE. Thirdly, this work extends the utilisation of QPP in UGS search to improve cross-lingual search for UGS by predicting the translation effectiveness. The thesis proposes novel methods to estimate the quality of translation for cross-lingual UGS search. An empirical evaluation that demonstrates the quality of the proposed method on alternative translation outputs extracted from several Machine Translation (MT) systems developed for this task. The research then shows how this framework can be integrated in cross-lingual UGS search to find relevant translations for improved retrieval performance

Recommender system performance evaluation and prediction: information retrieval perspective

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 201