This paper describes our system used in the ACL 2015 Workshop on Noisy Usergenerated Text Shared Task for Named Entity Recognition (NER) in Twitter. Our system uses Conditional Random Fields to train two separate classifiers for the two evaluations: predicting 10 fine-grained types, and segmenting named entities. We focus our efforts on generating word representations from large amount of unlab...

In this paper, we propose a multi-criteria based active learning approach and effectively apply it to named entity recognition. Active learning targets to minimize the human annotation efforts by selecting examples for labeling. To maximize the contribution of the selected examples, we consider the multiple criteria: informativeness, representativeness and diversity and propose measures to quan...

We describe the application of the LingPipe toolkit (Alias-i 2006) to Chinese word segmentation and named entity recognition. We provide results for the third SIGHAN Chinese language processing bakeoff (Levow 2006). F1 measures on the best performing corpora were .972 for word segmentation and .855 for person/location/organization named-

In this paper, we study the effects of various lemmatization and stemming approaches on the named entity recognition (NER) task for Czech, a highly inflectional language. Lemmatizers are seen as a necessary component for Czech NER systems and they were used in all published papers about Czech NER so far. Thus, it has an utmost importance to explore their benefits, limits and differences between...

We present a memory-based named entity recognition system that participated in the MSM-2013 Concept Extraction Challenge. The system expands the training set of annotated tweets with part-ofspeech tags and seedlist information, and then generates a sequential memory-based tagger comprised of separate modules for known and unknown words. Two taggers are trained: one on the original capitalized d...

We present an Extended Named Entity Recognition API to recognize various types of entities and classify the entities into 200 different categories. Each entity is classified into a hierarchy of entity categories, in which the categories near the root are more general than the categories near the leaves of the hierarchy. This category information can be used in various applications such as langu...

We present a phrase recognition system based on perceptrons, and an online learning algorithm to train them together. The recognition strategy applies learning in two layers, first at word level, to filter words and form phrase candidates, second at phrase level, to rank phrases and select the optimal ones. We provide a global feedback rule which reflects the dependencies among perceptrons and ...

RÉSUMÉ La portabilité entre les langues des systèmes de reconnaissance d’entités nommées est coûteuse en termes de temps et de connaissances linguistiques requises. L’adaptation des systèmes symboliques souffrent du coût de développement de nouveaux lexiques et de la mise à jour des règles contextuelles. D’un autre côté, l’adaptation des systèmes statistiques se heurtent au problème du coût de ...

In this paper, a pre-identification method for Chinese named entity recognition is proposed. Internal information of entity name like family name, first name in person name, feature word in place name and organization name do not needed. Through entity name guessing based on context keywords, pre-identification is realized. Definition of bidirectional potential entity name recognition, rough co...

This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so...