scispace - formally typeset
Search or ask a question
Topic

Document retrieval

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


Papers
More filters
Proceedings ArticleDOI
02 Nov 2009
TL;DR: A probabilistic retrieval model using the mapping relation between a query term and a document field (PRM-S) has the best performance in collections with more structure, such as email, and that the query-likelihood language model is better for other document types.
Abstract: Desktop search is an important part of personal information management (PIM). However, research in this area has been limited by the lack of shareable test collections, making cumulative progress difficult. In this paper, we define desktop search as a semi-structured document retrieval problem and introduce a methodology to automatically build a reusable collection (the pseudo-desktop) that has many of the same properties as a real desktop collection. We then present a comprehensive evaluation of retrieval methods for semi-structured document retrieval on several pseudo-desktop collections and the TREC Enterprise collection. Our results show that a probabilistic retrieval model using the mapping relation between a query term and a document field (PRM-S) has the best performance in collections with more structure, such as email, and that the query-likelihood language model is better for other document types. We further analyze the observed differences using generated queries and suggest ways to improve PRM-S, which makes the performance gains more significant and consistent.

52 citations

Journal ArticleDOI
TL;DR: The best kind of causal relation matching was found to be one in which one member of the causal relation was represented as a wildcard that could match with any word.
Abstract: This study attempted to use semantic relations expressed in text, in particular cause-effect relations, to improve information retrieval effectiveness. The study investigated whether the information obtained by matching cause-effect relations expressed in documents with the cause-effect relations expressed in users’ queries can be used to improve document retrieval results, in comparison to using just keyword matching without considering relations. An automatic method for identifying and extracting cause-effect information in Wall Street Journal text was developed. Causal relation matching was found to yield a small but significant improvement in retrieval results when the weights used for combining the scores from different types of matching were customized for each query. Causal relation matching did not perform better than word proximity matching (i.e. matching pairs of causally related words in the query with pairs of words that co-occur within document sentences), but the best results were obtained when causal relation matching was combined with word proximity matching. The best kind of causal relation matching was found to be one in which one member of the causal relation (either the cause or the effect) was represented as a wildcard that could match with any word.

52 citations

Journal ArticleDOI
John O'Connor1
TL;DR: The present experiment involved a greater variety of forms of retrieval question and search words were selected independently by two different people for each retrieval question, producing average recall ratios and false retrieval rates.
Abstract: Passage retrieval (already operational for lawyers) has advantages in output form over reference retrieval and is economically feasible. Previous experiments in passage retrieval for scientists have demonstrated recall and false retrieval rates as good or better than those of present reference retrieval services. The present experiment involved a greater variety of forms of retrieval question. In addition, search words were selected independently by two different people for each retrieval question. The search words selected, in combination with the computer procedures used for passage retrieval, produced average recall ratios of 72 and 67%, respectively, for the two selectors. The false retrieval rates were (except for one predictably difficult question) respectively 13 and 10 falsely retrieved sentences per answer-paper retrieved.

52 citations

Patent
22 Jul 2002
TL;DR: In this paper, a language model (114) is created for speech recognition from a text database (122) by an offline modeling processing (130) (solid line arrows), when a user talks to request for search, an acoustic model and the language model are used to perform a speech recognition processing and write-up is created.
Abstract: A language model (114) is created for speech recognition from a text database (122)by an offline modeling processing (130) (solid line arrows). In an online processing, when a user talks to request for search, an acoustic model (112) and the language model (114) are used to perform a speech recognition processing (110) and write-up is created. Next, by using the search request written up, a text search processing (120) is performed and the search result is output in the order of higher correlation.

52 citations


Network Information
Related Topics (5)
Web page
50.3K papers, 975.1K citations
81% related
Metadata
43.9K papers, 642.7K citations
79% related
Recommender system
27.2K papers, 598K citations
79% related
Ontology (information science)
57K papers, 869.1K citations
78% related
Natural language
31.1K papers, 806.8K citations
77% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20239
202239
2021107
2020130
2019144
2018111