Translation can have long-term effects on all languages and cultures. It is not a mere linguistic act, but mostly a cultural act, since language is by nature one of the major carriers of cultural elements. Thus, the translator’s job is not just transferring the meaning of words and sentences from the source text to the target text. Culture-specific items often cause translation problems. Identi...

This paper proposes and evaluates the use of linguistic information in the pre-processing phase of text mining tasks. We present several experiments comparing our proposal for selection of terms based on linguistic knowledge with usual techniques applied in the field. The results show that part of speech information is useful for the pre-processing phase of text categorization and clustering, a...

Support Vector Machines have been used successfully to classify text documents into sets of concepts. However, typically, linguistic information is not being used in the classification process or its use has not been fully evaluated. We apply and evaluate two basic linguistic procedures (stop-word removal and stemming/lemmatization) to the multilabel text classification problem. These procedure...

We describe the way we adapted a text analysis tool for annotating with the Linguistic Description Scheme of MPEG-7 text related to and extracted from multimedia content. Practically applied in the DIRECT-INFO EC R&D project we show how such linguistic annotation contributes to semantic annotation of multimodal analysis systems, demonstrating also the use of the XML schema of MPEG-7 for support...

Speech summarization is a technique of extracting important sentences from spoken documents. It provides us useful information to looking for the spoken documents that we want. Spoken documents contain non-linguistic information, which is mainly expressed by prosody, while written text conveys only linguistic information. This paper describes a summarization method which uses prosodic informati...

This paper describes the creation and content two corpora, TDT-2 and TDT-3, created for the DARPA sponsored Topic Detection and Tracking project. The research goal in the TDT program is to create the core technology of a news understanding system that can process multilingual news content categorizing individual stories according to the topic(s) they describe. The research tasks include segment...

Morphologically-rich languages pose problems for machine translation (MT) systems, including word-alignment errors, data sparsity and multiple affixes. Current alignment models at word-level do not distinguish words and morphemes, thus yielding low-quality alignment and subsequently affecting end translation quality. Models using morpheme-level alignment can reduce the vocabulary size of morpho...

Linguistic research has become heavily reliant on text corpora over the past ten years. Such resources are becoming increasingly available through efforts such as the Linguistic Data Consortium (LDC) in the US and the European Language Resources Association (ELRA) in Europe. However, in the main the corpora that are gathered and distributed through these and other mechanisms consist of texts wh...

The objective of the Topic Detection and Tracking (TDT) program is to develop technologies that search, organize and structure multilingual, news oriented textual materials from a variety of broadcast news media. This research program uses controlled laboratory simulations of hypothetical systems to test the efficacy of potential technologies, to gauge research progress, and to provide a forum ...