[PDF] Top 20 Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets

... 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

... on tweets was presented in [31] in which a KNN-based classifier and a CRF model were ...semi-supervised learning approach that combined the CRF model with a classifier based on the co-occurrence coefficient ... See full document

... tive learning, our method models two annotators: a reliable one and a fallible one, who have differ- ent probabilities of providing correct ...proactive learning, the reliable ex- pert is assumed to be ... See full document

... #2: Named Entity Recognition in ...word-level features, and which holds the current state-of-the-art on both newswire texts (CoNLL 2003) and diverse corpora including Web texts ... See full document

... Arabic named entities a challenging task, to which, the nature of tweets adds new ...in tweets de- grades the performance of NER systems de- veloped to support formal MSA ...from tweets ... See full document

... domain features in an unsupervised manner to make them ...of features from different do- mains may not be similar, which means that close source and target samples may not have the same ... See full document

... Named Entity Recognition is commonly formulated as a word-level tagging problem where each word in the sentence is mapped to a named entity ...extract features for ... See full document

... only entity disambiguation of previ- ously detected ...main features a candidate entity representation, a mention representation, and a representation of the mention’s context (Sun et ...global ... See full document

... the data is closely related both in domain and task, has actually hurt the performance of our recognizer for training sizes of moderate to large size. This is most likely because, although the MUC6 and MUC7 datasets are ... See full document

... The growth in neural approaches spurred a push towards “NLP from scratch”, that is, without en- gineering task- or language-specific features by hand (Collobert et al., 2011). Currently, mainly recurrent and/or ... See full document

... to transfer knowledge between domains, as related NE types often share lexical and context ...context features of PERSON and UNIVERSITY to induce a model for ... See full document

... identify named entities in a sentence using both word and character embeddings as ...input sentence, we propose to generate and feed an orthographic sentence into a bi-directional LSTM to ... See full document

... machine learning-based methods and deep learning-based ...machine learning-based methods (Settles, 2005; Campos et ...useful features using vari- ous NLP ...deep learning-based methods ... See full document

... context features as well as properties of the token ...non-local features (Ratinov and Roth, 2009) to improve supervised sequential models, the consensus seems to be that this non- local information has a ... See full document

... machine learning-based tools do not reach good levels of recall on tweets ...similar features to those used for POS tagging, and we have already demonstrated the significant impact that in-domain ... See full document

... Over the past several years, a series of papers have used deep neural networks (DNNs) to advance the state of the art in named entity recognition (NER) (Collobert et al., 2011; Huang et al., 2015; ... See full document

... Mongolian named entity recognition corpus and explored three morphological processing methods with different features combination under the CRF ...cluster features represent the latent ... See full document

... 2079 tweets (35,374 ...token level for three classical named entity types P erson, Location and Organisation, using the IOB ...2763 entity phrases (3751 tokens) which in- cluded 1,644 P ... See full document

... Element resolution (ER), otherwise called duplication discovery, record linkage, and so forth is the errand of discovering records alluding to a similar certifiable element in a dataset. It has been broadly contemplated ... See full document

... dependent features (based on gazetteers or morphological information) always increase NER accuracy; morphological information is inevitable for morphologically rich languages in order for the NER method to be ... See full document