Automatic Ranking of Retrieval Systems in Imperfect Environments

The empirical investigation of the effectiveness of information retrieval (IR) systems requires a test collection, a set of query topics, and a set of relevance judgments made by human assessors for each query. Previous experiments show that differences in human relevance assessments do not affect the relative performance of retrieval systems. Based on this observation, we propose and evaluate a new approach to replace the human relevance judgments by an automatic method. Ranking of retrieval systems with our methodology correlates positively and significantly with that of human-based evaluations. In the experiments, we assume a Web-like imperfect environment: the indexing information for all documents is available for ranking, but some documents may not be available for retrieval. Such conditions can be due to document deletions or network problems. Our method of simulating imperfect environments can be used for Web search engine assessment and in estimating the effects of network conditions (e.g., network unreliability) on IR system performance

Automatic performance evaluation of information retrieval systems using data fusion

Cataloged from PDF version of article.The empirical investigation of the effectiveness of information retrieval systems (search engines) requires a test collection composed of a set of documents, a set of query topics and a set of relevance judgments indicating which documents are relevant to which topics. The human relevance judgments are expensive and subjective. In addition to this databases and user interests change quickly. Hence there is a great need of automatic way of evaluating the performance of search engines. Furthermore, recent studies show that differences in human relevance assessments do not affect the relative performance of information retrieval systems. Based on these observations, in this thesis, we propose and use data fusion to replace human relevance judgments and introduce an automatic evaluation method and provide its comprehensive statistical assessment with several Text Retrieval Conference (TREC) systems which shows that the method results correlates positively and significantly with the actual human based evaluations. The major contributions of this thesis are: (1) an automatic information retrieval performance evaluation method that uses data fusion algorithms for the first time in the literature, (2) system selection methods for data fusion aiming even higher correlation among automatic and human-based results, (3) several practical implications stemming from the fact that the automatic precision values are strongly correlated with those of actual information retrieval systems.Nuray, RabiaM.S

The Use Of Kullback-Leibler Divergence In Opinion Retrieval

With the huge amount of subjective contents in on-line documents, there is a clear need for an information retrieval system that supports retrieval of documents containing opinions about the topic expressed in a user’s query. In recent years, blogs, a new publishing medium, have attracted a large number of people to express personal opinions covering all kinds of topics in response to the real-world events. The opinionated nature of blogs makes them a new interesting research area for opinion retrieval. Identification and extraction of subjective contents from blogs has become the subject of several research projects. In this thesis, four novel methods are proposed to retrieve blog posts that express opinions about the given topics. The first method utilizes the Kullback-Leibler divergence (KLD) to weight the lexicon of subjective adjectives around query terms. Considering the distances between the query terms and subjective adjectives, the second method uses KLD scores of subjective adjectives based on distances from the query terms for document re-ranking. The third method calculates KLD scores of subjective adjectives for predefined query categories. In the fourth method, collocates, words co-occurring with query terms in the corpus, are used to construct the subjective lexicon automatically. The KLD scores of collocates are then calculated and used for document ranking. Four groups of experiments are conducted to evaluate the proposed methods on the TREC test collections. The results of the experiments are compared with the baseline systems to determine the effectiveness of using KLD in opinion retrieval. Further studies are recommended to explore more sophisticated approaches to identify subjectivity and promising techniques to extract opinions

Automatic ranking of retrieval systems in imperfect environments

The empirical investigation of the effectiveness of information retrieval (IR) systems requires a test collection, a set of query topics, and a set of relevance judgments made by human assessors for each query. Previous experiments show that differences in human relevance assessments do not affect the relative performance of retrieval systems. Based on this observation, we propose and evaluate a new approach to replace the human relevance judgments by an automatic method. Ranking of retrieval systems with our methodology correlates positively and significantly with that of human-based evaluations. In the experiments, we assume a Web-like imperfect environment: the indexing information for all documents is available for ranking, but some documents may not be available for retrieval. Such conditions can be due to document deletions or network problems. Our method of simulating imperfect environments can be used for Web search engine assessment and in estimating the effects of network conditions (e.g., network unreliability) on IR system performance