Discriminative models have been preferred over generative models in many machine learning problems in the recent past owing to some of their attractive theoretical properties. In this paper, we explore the applicability of discriminative classifiers for IR. We have compared the performance of two popular discriminative models, namely the maximum entropy model and support vector machines with that of language modeling, the state-of-the-art generative model for IR. Our experiments on ad-hoc retrieval indicate that although maximum entropy is significantly worse than language models, support vector machines are on par with language models. We argue that the main reason to prefer SVMs over language models is their ability to learn arbitrary features automatically as demonstrated by our experiments on the home-page finding task of TREC-10.

Abstract not found

Introduction Recent advances in Information Retrieval are based on using Statistical Language Models (SLM) for representing documents and evaluating their relevance to user queries [6, 3, 4]. Language Modeling (LM) has been explored in many natural language tasks including machine translation and speech recognition [1]. In LM approach to document retrieval, each document, D, is viewed to have its own language model, MD . Given a query, Q, documents are ranked based on the probability, P (Q|MD ), of their language model generating the query. While the LM approach to information retrieval has been motivated from di#erent perspectives [3, 4], most experiments have used smoothed unigram language models that assume term independence for estimating document language models. N-gram, specifically, bigram language models that capture context provided by the previous word(s) perform better than unigram models [7]. Biterm language models [8] that ignore the word order constraint in bigram langu

In this thesis, we investigate using the Language Modeling approach for ad hoc Information Retrieval as a theoretically principled framework for encoding contextual evidence. Using context to improve retrieval performance is a current challenge within the discipline and presents a major challenge to the research community. The Language Modeling approach provides a natural and intuitive means of encoding the context as-sociated with a document. However, the Language Modeling approach also represents a change to the way probability theory is applied in ad hoc Information Retrieval and makes several assumptions for its application [112, 113, 57, 96]. We consider these assumptions and study them in detail during the course of this thesis. Central to the assumptions is the key implication that better retrieval performance can be obtained through developing better representation of the documents. We posit that the context associated with a document will enable the development of such representations-context based document models. This premise relies upon the explicit and implicit assumptions of the Language Modeling approach being valid, which have, up until now,

Question answering systems which automatically search for user’s information need are considered as a separate issue from the community-generated question answering which answers users ’ questions by human respondents. Although the two answering systems have different applications, both of them aim to present a correct answer to the users ’ question and consequently they can feed each other to improve their performance and efficiency. In this paper, we propose a new idea to use the information derived from a community question answering forum in an automatic question answering system. To this end, two different frameworks, namely the class-based model and the trained trigger model, have been used in a language model-based sentence retrieval system. Both models try to capture word relationships from the question-answer sentence pair of a community forum. Using a standard TREC question answering dataset, we evaluate our proposed models on the subtask of sentence retrieval, while training the models on the Yahoo! Answer corpus. Results show both methods that trained on Yahoo! Answers logs significantly outperform the unigram model, in which the class-based model achieved 4.72 % relative improvement in mean average precision and the trained triggering model achieved 18.10 % relative improvement in the same evaluation metric. Combination of both proposed models also improved the system mean average precision 19.29%. 1.