Справочники

We used the memory-based learner Timbl (Daelemans et al., 2002) to find names in En- glish and German newspaper text. A first sys- tem used only the training data, and …...

Much work on information extraction has successfully used gazetteers to recognise uncommon entities that cannot be reliably identified from local context alone. Ap- proaches to such tasks often involve the …...

The challenges of Named Entities Recogni- tion (NER) for tweets lie in the insufficient information in a tweet and the unavailabil- ity of training data. We propose to com- bine …...

This paper describes a temporal expression identification and normalization system, Man- TIME, developed for the TempEval-3 chal- lenge. The identification phase combines the use of conditional random fields along with …...

AbstractNamed entity recognition (NER) is a knowledge-intensive information extraction task that is used for recognizing textual mentions of entities that belong to a predefined set of categories, such as locations, …...

AbstractDespite many recent papers on Arabic Named Entity Recognition (NER) in the news domain, little work has been done on microblog NER. NER on microblogs presents many complications such as …...

In named entity recognition task especially for massive data like Twitter, having a large amount of high quality gazetteers can allevi- ate the problem of training data scarcity. One could …...

AbstractThis paper describes our submission for the ScienceIE shared task (SemEval- 2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation …...

AbstractExtracting location names from informal and unstructured social media data requires the identification of referent boundaries and partitioning compound names. Variability, particularly systematic variability in location names (Carroll, 1983), challenges …...

AbstractNeural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features. While some features do remain in state-of-the-art systems, lexical features have been mostly discarded, with the exception …...

AbstractGazetteers were shown to be useful resources for named entity recognition (NER). Many existing approaches to incorporating gazetteers into machine learning based NER systems rely on manually defined selection strategies …...

AbstractCurrent region-based NER models only rely on fully-annotated training data to learn effective region encoder, which often face the training data bottleneck. To alleviate this problem, this paper proposes Gazetteer-Enhanced …...

AbstractWe present AnonData, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing …...