Indexing by Latent Semantic Analysis
نویسندگان
چکیده
A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents ("semantic structure") in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. Initial tests find this completely automatic method for retrieval to be promising.
منابع مشابه
Using Random Indexing to improve Singular Value Decomposition for Latent Semantic Analysis
We present results from using Random Indexing for Latent Semantic Analysis to handle Singular Value Decomposition tractability issues. We compare Latent Semantic Analysis, Random Indexing and Latent Semantic Analysis on Random Indexing reduced matrices. In this study we use a corpus comprising 1003 documents from the MEDLINE-corpus. Our results show that Latent Semantic Analysis on Random Index...
متن کاملProbabilistic Latent Semantic Indexing Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval
Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain{speci c synonymy as well as with polysemous words. In contrast ...
متن کاملQuery expansion based on relevance feedback and latent semantic analysis
Web search engines are one of the most popular tools on the Internet which are widely-used by expert and novice users. Constructing an adequate query which represents the best specification of users’ information need to the search engine is an important concern of web users. Query expansion is a way to reduce this concern and increase user satisfaction. In this paper, a new method of query expa...
متن کاملLatent Semantic Indexing Based on Factor Analysis
The main purpose of this paper is to propose a novel latent semantic indexing (LSI), statistical approach to simultaneously mapping documents and terms into a latent semantic space. This approach can index documents more effectively than the vector space model (VSM). Latent semantic indexing (LSI), which is based on singular value decomposition (SVD), and probabilistic latent semantic indexing ...
متن کاملIndexing Audio Documents by using Latent Semantic Analysis and SOM
This paper describes an important application for state-of-art automatic speech recognition , natural language processing and information retrieval systems. Methods for enhancing the indexing of spoken documents by using latent semantic analysis and self-organizing maps are presented, motivated and tested. The idea is to extract extra information from the structure of the document collection an...
متن کاملLatent Semantic Indexing with a Variable Number of Orthogonal Factors
We seek insight into Latent Semantic Indexing by establishing a method to identify the optimal number of factors in the approximation matrix. We define some reasonable property for the approximation to hold and derive a new, un-parametric query expansion method. Extensive numerical experiments confirm the value of the new method.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- JASIS
دوره 41 شماره
صفحات -
تاریخ انتشار 1990