The two main factors that characterize a text are its content and its style, and both can be used as a means of categorization. In this paper we present an approach to text categorization in terms of genre and author for Modern Greek. In contrast to previous stylometric approaches, we attempt to take full advantage of existing natural language processing (NLP) tools. To this end, we propose a set of style markers including analysis-level measures that represent the way in which the input text has been analyzed and capture useful stylistic information without additional cost. We present a set of small-scale but reasonable experiments in text genre detection, author identification, and author verification tasks and show that the proposed method performs better than the most popular distributional lexical measures, i.e., functions of vocabulary richness and frequencies of occurrence of the most frequent words. All the presented experiments are based on unrestricted text downloaded from the World Wide Web without any manual text preprocessing or text sampling. Various performance issues regarding the training set size and the significance of the proposed style markers are discussed. Our system can be used in any application that requires fast and easily adaptable text categorization in terms of stylistically homogeneous categories. Moreover, the procedure of defining analysis-level markers can be followed in order to extract useful stylistic information using existing text processing tools.

/pdf/automatic-text-categorization-in-terms-of-genre-and-author-dlhj6fr633.pdf

Automatic text categorization in terms of genre and author

With the increase of information on the Web, it is difficult to find desired information quickly out of the documents retrieved by a search engine. One way to solve this problem is to classify web documents according to various criteria. Most document classification has been focused on a subject or a topic of a document. A genre or a style is another view of a document different from a subject or a topic. The genre is also a criterion to classify documents. In this paper, we suggest multiple sets of features to classify genres of web documents. The basic set of features, which have been proposed in the previous studies, is acquired from the textual properties of documents, such as the number of sentences, the number of a certain word, etc. However, web documents are different from textual documents in that they contain URL and HTML tags within the pages. We introduce new sets of features specific to web documents, which are extracted from URL and HTML tags. The present work is an attempt to evaluate the performance of the proposed sets of features, and to discuss their characteristics. Finally, we conclude which is an appropriate set of features in automatic genre classification of web documents.

Multiple sets of features for automatic genre classification of web documents

The aim of this paper is to explore text topic influence in authorship attribution. Specifically, we test the widely accepted belief that stylometric variables commonly used in authorship attribution are topic-neutral and can be used in multi-topic corpora. In order to investigate this hypothesis, we created a special corpus, which was controlled for topic and author simultaneously. The corpus consists of 200 Modern Greek newswire articles written by two authors in two different topics. Many commonly used stylometric variables were calculated and for each one we performed a two-way ANOVA test, in order to estimate the main effects of author, topic and the interaction between them. The results showed that most of the variables exhibit considerable correlation with the text topic and their exploitation in authorship analysis should be done with caution.

/pdf/investigating-topic-influence-in-authorship-attribution-2yvrhon8d9.pdf

Investigating Topic Influence in Authorship Attribution.

Digital libraries remove physical barriers to accessing information, but the language barrier still remains due to multilingual collections and the linguistic diversity of users. This study aims at understanding the effect of users' language skills and field of knowledge on their language preferences when searching for information online and to provide new insights on the access to multilingual digital libraries. Both quantitative and qualitative data were gathered using a questionnaire and results show that the language skills and the field of knowledge have an impact on the language choice for searching online. These factors also determine the interest in cross-language information retrieval: language-related fields constitute the best potential group of users, followed by the Arts and Humanities and the Social Sciences.

http://ir.shef.ac.uk/cloughie/papers/IJDLS_2010.pdf

Investigating Language Skills and Field of Knowledge on Multilingual Information Access in Digital Libraries

A genre or a style is another view of documents different from a subject or a topic. The genre is also a criterion to classify the documents. There have been several studies on detecting a genre of textual documents. However, only a few of them dealt with web documents. In this paper we suggest sets of features to detect genres of web documents. Web documents are different from textual documents in that they contain URL and HTML tags within the pages. We introduce the features specific to web documents, which are extracted from URL and HTML tags. Experimental results enable us to evaluate their characteristics and performances.

Automatic genre detection of web documents

The presented work is strongly motivated by the need for categorizing unrestricted text in terms of a functional style (FS) in order to attain a satisfying outcome in style processing. Towards this aim, a three level description of FS is given that comprises: (a) the basic categories of FS; (b) the main features that characterize each one of the above categories; and (c) the linguistic identifiers that act as style markers in text for the identification of the above features. Special emphasis is put on the problems that faced the computational implementation of the aforementioned findings, as well as the selection of the most appropriate stylometrics (i.e., stylistic scores) to achieve better results on text categorization. This approach is language independent, empirically driven, and can be used in various applications including grammar and style checking, natural language generation, style verification in real world text, and recognition of style shift between adjacent portions of text.

An empirical text categorizing computational model based on stylistic aspects

Language barriers present a major problemin the eA ectiveness of resource sharing and in common access to the resources of libraries. In this paper we present the TRANSLIB system, which consists of an integration of both new and existing multilingual information tools. This systemtakes full advantage of some AI± based methods in order to provide multilingual access to library catalogues. Its main features include functionalities for searching in multiple languages, multilingual presentation of the query results, and localization of the user interface. TRANSLIB has currently been tested in existing medium± sized bibliographic databases. Evaluation results show a remarkable improvement in the search process and report high user friendliness and easy and low± cost maintenance and upgrade of the system. Today’s libraries have automated their everyday transactions such as acqui± sition, cataloguing, and circulation. An automated library appears to users as an online public access catalogue (OPAC) through which they can quickly search and obtain the desired information (Leeves, 1994). Such systems support bibliographic search according to author name, title, pub± lication year, etc., and provide free text and keyword facilities. Hence, com± puter systems have replaced the librarian’s role as intermediary between user and library, and expert systems that simulate this role providing general information to the user have been developed (Morris, 1992). Moreover, the application of AI to information retrieval has recently attracted the attention of information scientists. Advanced applications of natural language processing (NLP), such as machine translation, have

Multilinguality in library automation systems using ai tools

Language barriers present a major problem in the effectiveness of resource sharing and in common access to the resources of libraries. In this paper we present the TRANSLIB system, which consists of an integration of both new and existing multilingual information tools. This system takes full advantage of some AI-based methods in order to provide multilingual access to library catalogues. Its main features include functionalities for searching in multiple languages, multilingual presentation of the query results, and localization of the user interface. TRANSLIB has currently been tested in existing medium-sized bibliographic databases. Evaluation results show a remarkable improvement in the search process and report high user friendliness and easy and low-cost maintenance and upgrade of the system.

Stephanos E. Michos

Papers

An empirical text categorizing computational model based on stylistic aspects

Multilinguality in library automation systems using ai tools

Supporting multilinguality in library automation systems using ai tools