Named entities (NE) mentioned in textual databases constitute an important part of their semantics. Lists of those NE are an important knowledge source for diverse tasks. We present a method for NE identification focused on composite proper names (names with coordinated constituents and names with several prepositional phrases.) We describe a method based on heterogeneous knowledge and simple r...

This paper describes our approaches for the preparation of gazetteers for named entity recognition (NER) in Indian languages. We have described two methodologies for the preparation of gazetteers1. Since the relevant gazetteer lists are more easily available in English we have used a transliteration based approach to convert available English name lists to Indian languages. The second approach ...

The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available onli...

The Europe Media Monitor (EMM) is a fully-automatic system that analyses written online news by gathering articles in over 70 languages and by applying text analysis software for currently 21 languages, without using linguistic tools such as parsers, part-of-speech taggers or morphological analysers. In this paper, we describe the effort of adding to EMM Hungarian text mining tools for news gat...

For the present work, we deal with the significant problem of high imbalance in data in binary or multi-class classification problems. We study two different linguistic applications. The former determines whether a syntactic construction (environment) co-occurs with a verb in a natural text corpus consists a subcategorization frame of the verb or not. The latter is called Name Entity Recognitio...

Named entity recognition for morphologically rich, case-insensitive languages, including the majority of semitic languages, Iranian languages, and Indian languages, is inherently more difficult than its English counterpart. Worse still, progress on machine learning approaches to named entity recognition for many of these languages is currently hampered by the scarcity of annotated data and the ...

We present a system to extract ranked lists of actors from fairytales ordered by importance. This task requires more than a straightforward application of generic methods such as Named Entity Recognition. We show that by focusing on two specific linguistic constructions that reflect the intentionality of a subject, direct and indirect speech, we obtain a high-precision method to extract the cas...