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- An Evaluation of Statistical Approaches to Text Categorization
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- Text Information Retrieval
- Text Information Retrieval
Suresh Babu, Mr.
An Evaluation of Statistical Approaches to Text Categorization
Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. Providing content-based access to large quantities of text is a difficult task, given our poor understanding of the formal semantics of human language.
The most successful approaches to retrieval, routing, and categorization of documents have relied heavily on statistical techniques. We briefly review some of those techniques and point out where better statistical insight could lead to further advances.
Information retrieval IR is concerned with providing access to data for which we do not have strong semantic models. Text is the most notable example, though voice, images, and video are of interest as well.
Examples of IR tasks include retrieving documents from a large database in response to immediate user needs, routing or filtering documents of interest from an ongoing stream over a period of time, and categorizing documents according to their content e.
Statistical approaches have been widely applied to these systems because of the poor fit of text to data models based on formal logics e. Rather than requiring that users anticipate exactly the words and combinations of words that will appear in documents of interest, statistical IR approaches let users simply list words that are likely to appear in relevant documents. The system then takes into account the frequency of these words in a collection of text, and in individual documents, to determine which words are likely to be the best clues of relevance.
A score is computed for each document based on the words it contains, and the highest scoring documents are retrieved, routed, categorized, etc. There are several variations on this approach [ 5 , 17 , 18 , 19 ]. Vector space models treat the words suggested by the user as specifying an ideal relevant document in a high dimensional space. The distance of actual documents to this point is used as a measure of relevance.
Probabilistic models attempt to estimate, for instance, the conditional probability of seeing particular words in relevant and nonrelevant documents. These estimates are combined under independence assumptions and documents are scored for probability of membership in the class of relevant documents. A variety of other formal and ad hoc statistical methods, including ones based on neural nets and fuzzy logic have been tried as well. In IR systems documents are often represented as vectors of binary or numeric values corresponding directly or indirectly to the words of the document.
Several properties of language, such as synonymy, ambiguity, and sheer variety make these representation far from ideal but also hard to improve on [ 13 ]. A variety of unsupervised learning methods have been applied to IR, with the hope of finding structure in large bodies of text that would improve on straightforward representations.
These include clustering of words or documents [ 10 , 20 ], factor analytic decompositions of term by document matrices [ 1 ], and various term weighting methods [ 16 ]. Similarly, the retrieval query, routing profile, or category description provided by an IR system user is often far from ideal as well.
Supervised learning techniques, where user feedback on relevant documents is used to improve the original user input, have been widely used [ 6 , 15 ]. Both parametric and nonparametric e. Supervised learning is particularly effective in routing where a user can supply ongoing feedback as the system is used [ 7 ] and in text categorization where a large body of manually indexed text may be available [ 12 , 14 ].
These are exciting times for IR. Until recently, IR researchers dealt mostly with relatively small and homogeneous collections of short documents often titles and abstracts.
Comparisons of over 30 IR. Much of this tuning has been ad hoc and heavily empirical. Little is known about the relationship between properties of a text base and the best IR methods to use with it.
This is an undesirable situation, given the increasing variety of applications IR is applied to, and is perhaps the most important area where better statistical insight would be helpful.
Four observations from the TREC conferences give a sense of the range of problems where better statistical insight is needed:. Others include dealing with time-varying streams of documents and time-varying user needs , drawing conclusions from databases that mix text and formatted data, and choosing what information sources to search in the first place.
On the tools side, a range of powerful techniques from statistics have seen relatively little application in IR, including cross-validation, model averaging, graphical models, hierarchical models, and many others. Curiously, highly computational methods have seen particularly little use. The author has been particularly interested in methods for actively selecting training data ala statistical design of experiments for supervised learning [ 9 , 11 ]. Since vast quantities of text are now cheap, while human time is expensive, these methods are of considerable interest.
In summary, the opportunities for and need of more statistical work in IR is as vast as the flood of online text engulfing the world! Dumais, George W.
Furnas, Thomas K. Landauer, and Richard Harshman. Indexing by latent semantic indexing. Harman, editor. National Institute of Standards and Technology. Special Publication Ranking algorithms.
In William B. Relevance feedback and other query modification techniques. Learning in intelligent information retrieval. Lewis and Jason Catlett. Heterogeneous uncertainty sampling for supervised learning. In William W. Morgan Kaufmann. Lewis and W. Bruce Croft. Term clustering of syntactic phrases.
Lewis and William A. A sequential algorithm for training text classifiers. Bruce Croft and C. Lewis and Philip J. Guest editorial. Lewis and Karen Sparck Jones. Natural language processing for information retrieval.
Communications of the ACM , To appear. Lewis and Marc Ringuette. A comparison of two learning algorithms for text categorization. ISRI; Univ. Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science , 41 4 , Term-weighting approaches in automatic text retrieval. Information Processing and Management , 24 5 , Introduction to Modern Information Retrieval.
Turtle and W. A comparison of text retrieval models. The Computer Journal , 35 3 , Information Retrieval. Butterworths, London, second edition, Recent trends in hierarchic document clustering: A critical review. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website. Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.
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Get This Book. Visit NAP. Looking for other ways to read this? No thanks. Massive Data Sets: Proceedings of a Workshop. Page Share Cite. More surprisingly, applying supervised learning methods to the top ranked documents from an initial retrieval run, as if they were known to be relevant, has been found to be somewhat useful. This strategy had failed in all attempts prior to TREC.
Is the size of the TREC collection the key to success? Can this idea be better understood and improved on perhaps using EM methods?
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Text Data Management and Analysis A Practical Introduction to Information Retrieval and Text Mining ChengXiang Zhai and Sean Massung Text Data Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as Management and Analysis blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people manage and analyze vast amounts of text data ef-. Text Data Management fectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and capture semantically rich content. As such, text. A Practical Introduction data are especially valuable for discovering knowledge about human opinions and preferenc- es, in addition to many other kinds of knowledge that we encode in text.
The first is information retrieval systems which include search engines and recommender systems; they assist users in finding from a large collection of text data.
Text Information Retrieval
Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. Providing content-based access to large quantities of text is a difficult task, given our poor understanding of the formal semantics of human language. The most successful approaches to retrieval, routing, and categorization of documents have relied heavily on statistical techniques. We briefly review some of those techniques and point out where better statistical insight could lead to further advances.
This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. Analysis and empirical evidence suggest that the evaluation results on some versions of Reuters were significantly affected by the inclusion of a large portion of unlabelled documents, mading those results difficult to interpret and leading to considerable confusions in the literature. Using the results evaluated on the other versions of Reuters which exclude the unlabelled documents, the performance of twelve methods are compared directly or indirectly. As a global observation, kNN, LLSF and a neural network method had the best performance; except for a Naive Bayes approach, the other learning algorithms also performed relatively well.
Text Information Retrieval
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets.
Natalia Prytkova. Asia Joanna Biega. The dates are preliminary. Type of the exam is currently planned to be oral. Information Retrieval IR and Data Mining DM are methodologies for organizing, searching and analyzing digital contents from the web, social media and enterprises as well as multivariate datasets in these contexts. IR models and algorithms include text indexing, query processing, search result ranking, and information extraction for semantic search.
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Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Text analysis and information retrieval of text data Abstract: Text summarization combines the process of POS tagging, term frequency and topical analysis. The concise version of the text document can be made using the concept of frequency of the terms and inverse frequency of documents. Text summarization is useful for bring the short story of all the newspaper articles, email correspondence or to extract key elements for the search engine. To compact the size, the sentences which are not near to the centroid is not to be considered in the output.
It emphasizes the most useful knowledge and skills required to build a variety of practically useful text information systems. Because humans can understand natural languages far better than computers can, effective involvement of humans in a text information system is generally needed and text information systems often serve as intelligent assistants for humans. Depending on how a text information system collaborates with humans, we distinguish two kinds of text information systems. The first is information retrieval systems which include search engines and recommender systems; they assist users in finding from a large collection of text data the most relevant text data that are actually needed for solving a specific application problem, thus effectively turning big raw text data into much smaller relevant text data that can be more easily processed by humans. The second is text mining application systems; they can assist users in analyzing patterns in text data to extract and discover useful actionable knowledge directly useful for task completion or decision making, thus providing more direct task support for users.
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