CiteULike: akshat's library [20 articles]

Sets of Sets: A Cognitive Obstacle

Lawrence Brenton — 2008-05-07T01:00:30-00:00

The College Mathematics Journal, Vol. 34, No. 1. (2003), pp. 31-38.

Hierarchical structure and the prediction of missing links in networks

Aaron Clauset — 2008-04-30T19:31:59-00:00

Nature, Vol. 453, No. 7191., pp. 98-101.

Text classification in a hierarchical mixture model for small training sets

Kristina Toutanova — 2006-11-13T23:45:43-00:00

(2001), pp. 105-113.

A Practical part-of-speech tagger

Doug Cutting — 2005-10-09T09:40:12-00:00

(1992)

We present an implementation of a part-of-speech tagger based on a hidden Markov model. The methodology enables robust and accurate tagging with few resource requirements. Only a lexicon and some unlabeled training text are required. Accuracy exceeds 96%. We describe implementation strategies and optimizations which result in high-speed operation. Three applications for tagging are described: phrase recognition; word sense disambiguation; and grammatical function assignment.

FACILE: Description of the NE System Used for MUC

W Black — 2008-05-03T13:35:21-00:00

(1998)

this paper, we describe the system used by the UMIST team as members of the FACILE consortium, to undertake the NE task in MUC-7. The main characteristics of this system employed are as follows: ffl it is rule-based ffl its rule formalism supports context-sensitive partial parsing ffl rules may use pattern-matching-style iteration operators ffl the notation is much more readable than classic pattern-matching languages ffl rules can be assigned an explicit weight which is used in choosing...

Cluster Analysis and Classification of Named Entities

Jf — 2008-05-03T13:30:31-00:00

Proc. Conference on Language Resources and Evaluation (2004)

A comparison of two approaches to fitting directed graphs to nonsymmetric proximity measures

K Klauer — 2008-05-03T13:24:52-00:00

Journal of Classification, Vol. 8, No. 2. (1 December 1991), pp. 251-268.

Abstract Two algorithms for fitting directed graphs to nonsymmetric proximity data are compared. The first approach, termed MAPNET, is a direct extension of a mathematical programming procedure for fitting undirected graphs to symmetric proximity data presented by Klauer and Carroll (1989). For a user-specified number of links, the algorithm seeks to provide the connected network that gives the least-squares approximation of the proximity data with the specified number of links, allowing for linear transformations of the data. The mathematical programming approach is compared to the NETSCAL method for fitting directed graphs (Hutchinson 1989), using the Monte Carlo methods and data sets employed by Hutchinson.

GroupMe! - Where Semantic Web Meets Web 2.0

Fabian Abel — 2008-05-03T13:12:08-00:00

The Semantic Web (2008), pp. 871-878.

Grouping is an attractive interaction metaphor for users to create reference collections of Web resources they are interested in. Each grouping activity has a certain semantics: things which were previously unrelated are now connected with others via the group. We present the GroupMe! application which allows users to group and arrange multimedia Web resources they are interested in. GroupMe! has an easy-to-use interface for gathering and grouping of resources, and allows users to tag everything they like. The semantics of any user interaction is captured, transformed and stored as adequate RDF descriptions. As an example application of this automatically derived RDF content, we show the enhancement of search for tagged Web resources, which evaluates the grouping information to deduce additional contextual information about the resources. GroupMe! is available via http://www.groupme.org .

The two cultures: Mashing up Web 2.0 and the Semantic Web

Anupriya Ankolekar — 2008-02-28T00:51:52-00:00

Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 6, No. 1. (February 2008), pp. 70-75.

A common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. A closer look, though, reveals that the core technologies and concerns of these two approaches are complementary and that each field can and must draw from the other's strengths. We believe that future Web applications will retain the Web 2.0 focus on community and usability, while drawing on Semantic Web infrastructure to facilitate mashup-like information sharing. However, there are several open issues that must be addressed before such applications can become commonplace. In this paper, we outline a semantic weblogs scenario that illustrates the potential for combining Web 2.0 and Semantic Web technologies, while highlighting the unresolved issues that impede its realization. Nevertheless, we believe that the scenario can be realized in the short-term. We point to recent progress made in resolving each of the issues as well as future research directions for each of the communities.

ECML/PKDD Discovery Challenge 2006

2008-05-03T13:10:07-00:00

(2006)

Learning to extract symbolic knowledge from the World Wide Web

M Craven — 2006-10-16T16:42:33-00:00

(1998)

The goal of the Web-KB project is to develop automatic methods for constructing and maintaining large knowledge bases whose contents mirror those of the World Wide Web. We argue for the feasibility of a system which, given a manually constructed ontology and a seed knowledge base comprising a set of labeled Web pages, learns to instantiate knowledge-base objects and relations from the Web. Such a system could construct a knowledge base supporting concept-oriented queries to the Web, or serve as ...

NewsWeeder: learning to filter netnews

Ken Lang — 2008-05-03T10:48:44-00:00

(1995), pp. 331-339.

A significant problem in many information filtering systems is the dependence on the user for the creation and maintenance of a user profile, which describes the user's interests. NewsWeeder is a netnews-filtering system that addresses this problem by letting the user rate his or her interest level for each article being read (1-5), and then learning a user profile based on these ratings. This paper describes how NewsWeeder accomplishes this task, and examines the alternative learning methods...

Introduction to Information Retrieval

Christopher — 2007-09-25T14:06:34-00:00

(2008)

As We May Think

Vannevar Bush — 2005-12-27T10:57:26-00:00

Atlantic Monthly (1945)

Interface Culture : How New Technology Transforms the Way We Create and Communicate

Steven Johnson — 2005-05-17T10:35:31-00:00

(06 October 1999)

Text classification and segmentation using minimum cross-entropy

William Teahan — 2008-05-03T05:07:54-00:00

(2000)

Several methods for classifying and segmenting text are described. These are based on ranking text sequences by their cross-entropy calculated using a fixed order character-based Markov model adapted from the PPM text compression algorithm. Experimental results show that the methods are a signi cant improvement over previously used methods in a number of areas. For example, text can be classified with a very high degree of accuracy by authorship, language, dialect and genre. Highly accurate...

Machine learning in automated text categorization

Fabrizio Sebastiani — 2006-11-15T16:18:54-00:00

ACM Computing Surveys, Vol. 34, No. 1. (2002), pp. 1-47.

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the...

Language and task independent text categorization with simple language models

P Fuchun — 2008-05-03T05:07:23-00:00

We present a simple method for language independent and task independent text categorization learning, based on character-level n-gram language models. Our approach uses simple information theoretic principles and achieves effective performance across a variety of languages and tasks without requiring feature selection or extensive pre-processing. To demonstrate the language and task independence of the proposed technique, we present experimental results on several languages - Greek, English,...

Criterion functions for document clustering: Experiments and analysis

Y Zhao — 2006-02-27T16:11:33-00:00

(2001)

In recent years, we have witnessed a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intranets. This has led to an increased interest in developing methods that can help users to effectively navigate, summarize, and organize this information with the ultimate goal of helping them to find what they are looking for. Fast and high-quality document clustering algorithms play an important role towards this goal as...

On Compression-Based Text Classification

Yuval Marton — 2008-05-03T00:05:35-00:00

Advances in Information Retrieval (2005), pp. 300-314.

Compression-based text classification methods are easy to apply, requiring virtually no preprocessing of the data. Most such methods are character-based, and thus have the potential to automatically capture non-word features of a document, such as punctuation, word-stems, and features spanning more than one word. However, compression-based classification methods have drawbacks (such as slow running time), and not all such methods are equally effective. We present the results of a number of experiments designed to evaluate the effectiveness and behavior of different compression-based text classification methods on English text. Among our experiments are some specifically designed to test whether the ability to capture non-word (including super-word) features causes character-based text compression methods to achieve more accurate classification.