A survey on the use of relevance feedback for information access systems

Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems

Machine Learning of User Profiles: Representational Issues

As more information becomes available electronically, tools for finding information of interest to users becomes increasingly important. The goal of the research described here is to build a system for generating comprehensible user profiles that accurately capture user interest with minimum user interaction. The research described here focuses on the importance of a suitable generalization hierarchy and representation for learning profiles which are predictively accurate and comprehensible. In our experiments we evaluated both traditional features based on weighted term vectors as well as subject features corresponding to categories which could be drawn from a thesaurus. Our experiments, conducted in the context of a content-based profiling system for on-line newspapers on the World Wide Web (the IDD News Browser), demonstrate the importance of a generalization hierarchy and the promise of combining natural language processing techniques with machine learning (ML) to address an information retrieval (IR) problem.Comment: 6 page

Augmenting Latent Dirichlet Allocation and Rank Threshold Detection with Ontologies

In an ever-increasing data rich environment, actionable information must be extracted, filtered, and correlated from massive amounts of disparate often free text sources. The usefulness of the retrieved information depends on how we accomplish these steps and present the most relevant information to the analyst. One method for extracting information from free text is Latent Dirichlet Allocation (LDA), a document categorization technique to classify documents into cohesive topics. Although LDA accounts for some implicit relationships such as synonymy (same meaning) it often ignores other semantic relationships such as polysemy (different meanings), hyponym (subordinate), meronym (part of), and troponomys (manner). To compensate for this deficiency, we incorporate explicit word ontologies, such as WordNet, into the LDA algorithm to account for various semantic relationships. Experiments over the 20 Newsgroups, NIPS, OHSUMED, and IED document collections demonstrate that incorporating such knowledge improves perplexity measure over LDA alone for given parameters. In addition, the same ontology augmentation improves recall and precision results for user queries

A Useful Framework for Identification and Analysis of Different Query Expansion Approaches based on the Candidate Expansion Terms Extraction Methods

Query expansion is a method for improving retrieval performance by supplementing an original query with additional terms. This process improves the quality of search engine results and helps users to find the required information. In the recent years, different methods have been proposed in this area. In addition to such a variety of different approaches in this area and necessity of the study of their characteristics, the lack of a comprehensive classification based on candidate expansion terms extraction methods and also suitable and complete criteria to evaluate them, make the precise study, comparison and evaluation of methods for query expansion and choosing appropriate method based on need difficult for researchers. Therefore, in this paper a new useful framework is presented. In the proposed framework, in addition to the identification of three basic approaches based on the candidate expansion terms extraction methods for query expansion and expressing their properties, appropriate criteria for qualitative evaluation of these methods will be described. Next, the proposed approaches will be evaluated qualitatively based on these criteria. Using the systematic and structured framework proposed in this paper leads a useful platform for researchers to be provided for the comparative study of existing methods in the field, investigating their features specially their drawbacks to improve them and choosing appropriate method based on their needs