[PDF] Top 20 Chinese Named Entity Recognition with Multiple Features

... hybrid Chinese NER model which combines multiple ...intrinsic features in different types of entities, we design several sub-models for different ... See full document

... to Chinese word ...view Chinese words as segmentation units whose definition is pragmatic in nature and depends on how they are used and processed (differently) in realistic applications, while theoretical ... See full document

... different features on the result, some experiments are conducted on these templates, with 90% of the training data provided by The SIGHAN Bakeoff 2007 for training, leaving 10% for ... See full document

... Named Entity Recognition (NER) is an impor- tant Natural Language Processing task. Its goal is to tag entities such as names of people and locations in text. State-of-the-art systems can achieve ... See full document

... hand-crafted features with ...tactic features, which takes less time for training and inference than Bi-LSTM-CNN-CRF model and does not depend on many hand-crafted features as ... See full document

... missed named entities; (2) The times of missing occurrences; (3) The number of different missed named entities which are detected correctly at least once; (4) The times missing occurrences under the case of ... See full document

... solve named entity recognition tasks for such widely used languages as English, there is no clear answer which methods are the most suitable for languages that are substantially ...a named ... See full document

... Multi-Grained Named Entity Recognition where multiple entities or en- tity mentions in a sentence could be non- overlapping or totally ...on multiple granularities: it is able to ... See full document

... In this paper, we present a new corpus for Chinese Named Entity Recognition in multi-domains, named M- CNER. We create several data sets in three domains: human-computer interaction, ... See full document

... It is difficult to tell if the difference between the SG and SG-g experiments are because of some vari- ation in the random initialization of the weights, random dropout between the layers, or some other variant. As we ... See full document

... each named en- tity corresponding to a phrase in the tree, along with a root node which connects the entire sen- ...a named entity ...build features over the labels, unary rules, and binary ... See full document

... Not much work has been done in NER in Telugu and other Indian languages so far. In this paper, we have reported our work on Named Entity Recogni- tion for Telugu. We have developed a CRF based noun tagger, ... See full document

... of Named Entity Recognition (NER) is to identify in text predefined units of information such as person names, organizations and ...specific features, the representation of named ... See full document

... NER model, Peng and Dredze (2016) co-trained NER and word segmentation to improve perfor- mance in both tasks. He and Sun (2017b) uni- fied cross-domain learning and semi-supervised learning to obtain information from ... See full document

... We do not take the advantage of using the golden- standard word segmentation and POS tagging provided in the original corpus, since such information is hardly avail- able in real text. Instead, we use an off-the-shelf ... See full document

... the entity boundary partition error, entity recognition in- complete and other dominating ones, drug dictionary, post-processing rules and entity auto-correct algorithm are taken as auxiliary ... See full document

... We explore several types of embeddings for Chinese text and their effect on Chinese social media NER. Specifically, we make the following contributions. 1) We present the first system for NER on ... See full document

... Text classification problems and algorithms have been around for a while now. The performance of a text classification model is heavily dependent upon the type of words used in the corpus and type of features ... See full document

... annotated named entity corpus of ...comprehensive features, including syllable features, lexical features, context features, morphological features and semantic ... See full document

... the Named Entity Recognition and Disam- biguation Task, given a query that consists of a name string-which can be a person (PER), organ- ization (ORG), location (LOC) or just common words- and a ... See full document