We report results using a hidden Markov model to extract information from broadcast news. IdentiFinderTM was trained on the broadcast news corpus and tested on both the 1996 HUB-4 development test data and the 1997 HUB-4 evaluation test data with respect to the named entity (NE) task: extracting • names of locations, persons, and organizations; • dates and times; • monetary amounts and percenta...

Document retrieval The entire text corpus was indexed using the Lucene document retrieval package1. As the first step in processing a query, Lucene was invoked to retrieve the documents containing the query name; a maximum of 500 documents were retrieved. No attempt was made to use the document associated with the name in the query in order to disambiguate possibly ambiguous names. As a minimal...

Recent years, big data has attracted increasing interest. Sentiment analysis from microblog as one kind of big data also receive great attention. Some recent research works are not suitable for sentiment analysis as the result that users prefer to express their feelings in individual ways. In this paper, a framework is proposed to calculate sentiment for aspects of event. Based on some state of...

This paper describes a co-training framework in which, through named entity aligned bilingual text, named entity taggers can complement and improve each other via an iterative process. This co-training approach allows us to 1) apply our method to not only parallel but also comparable text, greatly extending the applicability of the approach; and to 2) adapt named entity taggers to new domains; ...

In legal domain Name Entity Recognition serves as the basis for subsequent stages of artificial intelligence. this paper, authors have developed a dataset training (NER) in Indian domain. As first step research methodology study is done to identify and establish more entities than commonly used named such person, organization, location, so on. The annotators can make use these annotate differen...

Name matching between multiple natural languages is an important step in cross-enterprise integration applications and data mining. It is difficult to decide whether or not two syntactic values (names) from two heterogeneous data sources are alternative designation of the same semantic entity (person), this process becomes more difficult with Arabic language due to several factors including spe...

One of the major goals of Spoken Dialog Systems (SDS) is to understand what the user utters. In the SDS domain, the Spoken Language Understanding (SLU) Module classifies user utterances by means of a pre-definite conceptual knowledge. The SLU module is able to recognize only the meaning previously included in its knowledge base. Due the vastity of that knowledge, the information storing is a ve...

The exponential growth of large-scale molecular sequence data and of the PubMed scientific literature has prompted active research in biological literature mining and information extraction to facilitate genome/proteome annotation and improve the quality of biological databases. Motivated by the promise of text mining methodologies, but at the same time, the lack of adequate curated data for tr...

This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for d...