We present a named entity recognition (NER) system for tagging fiction: LitNER. Relative to more traditional approaches, LitNER has two important properties: (1) it makes no use of handtagged data or gazetteers, instead it bootstraps a model from term clusters; and (2) it leverages multiple instances of the same name in a text. Our experiments show it to substantially outperform off-the-shelf s...

This paper explores the possible role of named entities in an automatic indexing process, based on text in subtitles. This is done by analyzing entity types, name density and name frequencies in subtitles and metadata records from different TV programs. The name density in metadata records is much higher than the name density in subtitles, and named entities with high frequencies in the subtitl...

Foreign name expressions written in Chinese characters are difficult to recognize since the sequence of characters represents the Chinese pronunciation of the name. This paper suggests that known English or German person names can reliably be identified on the basis of the similarity between the Chinese and the foreign pronunciation. In addition to locating a person name in the text and learnin...

Japanese was one of the languages selected for evaluation of named entity identification algorithms in the TIPSTER-sponsored Multilingual Entity Task (MET) program. As with the Spanish and Chinese groups (Table 1), Japanese systems automatically marked the names of organizations, people, and places within entity name expressions (ENAMEX), dates and times within time expressions (TIMEX), and per...

This paper describes the development of CICEROARABIC, the first wide coverage named entity recognition (NER) system for Modern Standard Arabic. Capable of classifying 18 different named entity classes with over 85% F, CICEROARABIC utilizes a new 800,000word annotated Arabic newswire corpus in order to achieve high performance without the need for hand-crafted rules or morphological information....

In this work, we present new state-ofthe-art results of 93.59% and 79.59% for Turkish and Czech named entity recognition based on the model of (Lample et al., 2016). We contribute by proposing several schemes for representing the morphological analysis of a word in the context of named entity recognition. We show that a concatenation of this representation with the word and character embeddings...