About: Concept search is a(n) research topic. Over the lifetime, 4041 publication(s) have been published within this topic receiving 125255 citation(s).
•01 Jan 2008
Abstract: Class-tested and coherent, this groundbreaking new textbook teaches web-era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.
TL;DR: A new conceptual paradigm for performing search in context is presented, that largely automates the search process, providing even non-professional users with highly relevant results.
Abstract: Keyword-based search engines are in widespread use today as a popular means for Web-based information retrieval Although such systems seem deceptively simple, a considerable amount of skill is required in order to satisfy non-trivial information needs This paper presents a new conceptual paradigm for performing search in context, that largely automates the search process, providing even non-professional users with highly relevant results This paradigm is implemented in practice in the IntelliZap system, where search is initiated from a text query marked by the user in a document she views, and is guided by the text surrounding the marked query in that document (“the context”) The context-driven information retrieval process involves semantic keyword extraction and clustering to automatically generate new, augmented queries The latter are submitted to a host of general and domain-specific search engines Search results are then semantically reranked, using context Experimental results testify that using context to guide search, effectively offers even inexperienced users an advanced search tool on the Web
••01 Sep 1999
TL;DR: It is shown that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query, suggesting that traditional information retrieval techniques may not work well for answering web search requests.
Abstract: In this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents almost 285 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplication, and query sessions. We also present results of a correlation analysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ significantly from the user assumed in the standard information retrieval literature. Specifically, we show that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query. This suggests that traditional information retrieval techniques may not work well for answering web search requests. The correlation analysis showed that the most highly correlated items are constituents of phrases. This result indicates it may be useful for search engines to consider search terms as parts of phrases even if the user did not explicitly specify them as such.
•17 Jun 1999
TL;DR: 1. Visual Information Retrieval 2. Image Retrival by Color Similarity 3. Image retrieval by Texture Similarity 4. image Retrieva by Shape Similarity 5. imageRetrievalBy Spatial Relationships 6. Content-Based Video Retrivel
Abstract: 1 Visual Information Retrieval 2 Image Retrieval by Color Similarity 3 Image Retrieval by Texture Similarity 4 Image Retrieval by Shape Similarity 5 Image Retrieval by Spatial Relationships 6 Content-Based Video Retrieval
••25 Oct 2010
TL;DR: It is shown that accounting for cross-modal correlations and semantic abstraction both improve retrieval accuracy and are shown to outperform state-of-the-art image retrieval systems on a unimodal retrieval task.
Abstract: The problem of joint modeling the text and image components of multimedia documents is studied. The text component is represented as a sample from a hidden topic model, learned with latent Dirichlet allocation, and images are represented as bags of visual (SIFT) features. Two hypotheses are investigated: that 1) there is a benefit to explicitly modeling correlations between the two components, and 2) this modeling is more effective in feature spaces with higher levels of abstraction. Correlations between the two components are learned with canonical correlation analysis. Abstraction is achieved by representing text and images at a more general, semantic level. The two hypotheses are studied in the context of the task of cross-modal document retrieval. This includes retrieving the text that most closely matches a query image, or retrieving the images that most closely match a query text. It is shown that accounting for cross-modal correlations and semantic abstraction both improve retrieval accuracy. The cross-modal model is also shown to outperform state-of-the-art image retrieval systems on a unimodal retrieval task.