Document retrieval

About: Document retrieval is a research topic. Over the lifetime, 6821 publications have been published within this topic receiving 214383 citations.

Biterm language models for document retrieval

Abstract: Statistical Language Models(LM) have been used in many natural language processing tasks including speech recognition and machine translation [5, 2]. Recently language models have been explored as a framework for information retrieval [9, 4, 7, 1, 6]. The basic idea is to view each document to have its own language model and model querying as a generative process. Documents are ranked based on the probability of their language model generating the given query. Since documents are fixed entities in information retrieval, language models for documents suffer from sparse data problem. Smoothed unigram models have been used to demonstrate better performance of language models against vector space or probabilistic retrieval models for document retrieval. Song and Croft [10] proposed a general language model that combined bigram language models with Good-Turing estimate and corpus-based smoothing of unigram probabilities. Improved performance was observed with combined bigram language models. The language models explored for information retrieval mimic those used for speech recognition. Specifically, in the bigram model a document d represented as word sequence w1, w2, · · · , wn is modeled as

Process and system for sparse vector and matrix representation of document indexing and retrieval

Abstract: A new data structure and algorithms which offer at least equal performance in common sparse matrix tasks, and improved performance in many. This is applied to a word-document index to produce fast build and query times for document retrieval.

Document retrieval system

Abstract: A document retrieval system employs a keyword connection table which contains relation information of keyword connections respectively coupling two arbitrary keywords which are used for retrieving registered documents. The relation information at least includes a relation name and a relationship describing the relation between the two arbitrary keywords. The relation information may dynamically change depending on a frequency of use of the keywords, that is, by a learning function.

Document representation and query expansion models for blog recommendation

Abstract: We explore several different document representation models and two query expansion models for the task of recommending blogs to a user in response to a query. Blog relevance ranking differs from traditional document ranking in ad-hocinformation retrieval in several ways: (1) the unit of output (the blog) is composed of a collection of documents (the blog posts) rather than a single document, (2) the query represents an ongoing and typically multifaceted interest in the topic rather than a passing ad-hoc information need and (3) due to the propensity of spam, splogs, and tangential comments, the blogosphere is particularly challenging to use as a source for high-quality query expansion terms. We address these differences at the document representation level, by comparing retrieval models that view either the blog or its constituent posts as the atomic units of retrieval, and at the query expansion level, by making novel use of the links and anchor text in Wikipedia1 to expand a user's initial query. We develop two complementary models of blog retrieval that perform at comparable levels of precision and recall. We also show consistent and significant improvement across all models using our Wikipedia expansion strategy.

Speech translation apparatus and computer program product

Abstract: A translation direction specifying unit specifies a first language and a second language. A speech recognizing unit recognizes a speech signal of the first language and outputs a first language character string. A first translating unit translates the first language character string into a second language character string that will be displayed on a display device. A keyword extracting unit extracts a keyword for a document retrieval from the first language character string or the second language character string, with which a document retrieving unit performs a document retrieval. A second translating unit translates a retrieved document into its opponent language, which will be displayed on the display device.