[PDF] Top 20 Learning to Search for Recognizing Named Entities in Twitter
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Learning to Search for Recognizing Named Entities in Twitter
... Table 4 presents the results in the same fashion as previously for the entities classification task. We include a version of our system without using the LOD features and a version without normalization in order ... See full document
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A Self learning Template Approach for Recognizing Named Entities from Web Text
... There have been many approaches proposed to solve the problem of the lack of annotated da- ta. (Wu et al., 2009; Chiticariu et al., 2010) fo- cus on domain adaptation, which aims to reuse the knowledge among different ... See full document
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Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
... machine learning model, we ran a parameter search on the CoNLL development ...the learning rate β = ...machine learning model using the training set and reported its performance using the test ... See full document
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The role of named entities in Web People Search
... Our aim is to reach conclusions which are are not tied to a particular choice of Clustering or Ma- chine Learning algorithms. We have taken two de- cisions in this direction: first, we have focused on the problem ... See full document
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Learning to recognise named entities in tweets by exploiting weakly labelled data
... specifically, learning rate and ...local search within a small interval was carried out, setting the other parameters to arbitrarily determined fixed ...values. Learning rate controls the step size ... See full document
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Recall Oriented Learning of Named Entities in Arabic Wikipedia
... Recognizing this limitation, some work on NER has sought to codify more robust invento- ries of general-purpose entity types (Sekine et al., 2002; Weischedel and Brunstein, 2005; Grouin et al., 2011) or to ... See full document
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Learning the Species of Biomedical Named Entities from Annotated Corpora
... For example, searching for the string plk1 in the above sen- tence in RefSeq 1 resulted in 98 hits, whereas when a species (e.g., mouse (Mus musculus) was added to the query, we were able to narrow down the number of ... See full document
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Mining named entities from search engine query logs
... Early work in NER was usually performed with hand- crafted rules [11], or with supervised learning techniques, where an annotated corpus (e.g., CoNLL-2003 consisting of news articles) was used as a training ... See full document
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Recognizing Relation Expression between Named Entities based on Inherent and Context dependent Features of Relational words
... supervised learning method cited in (Kambhatla, 2004; Culotta and Sorensen, 2004; Zelenko et ...related named entity pairs and to recognize the relation between them with- out using predefined ... See full document
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Recognizing Biomedical Named Entities Using Skip Chain Conditional Random Fields
... machine learning techniques gener- ally obtains superior performance for the biomedi- cal NER ...automated learning process can induce patterns for recognizing biomedical names and rules for pre- and ... See full document
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Recognizing Named Entities in Tweets
... We propose a novel NER system to address these challenges. Firstly, a K-Nearest Neighbors (KNN) based classifier is adopted to conduct word level classification, leveraging the similar and recently labeled tweets. ... See full document
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On Jointly Recognizing and Aligning Bilingual Named Entities
... semi-supervised learning after convergence for adopting only the English NER model (NER-Only), the baseline alignment model (NER+Baseline), and our un-weighted joint model (NER+JointModel) ... See full document
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What’s in a Name? Entity Type Variation across Two Biomedical Subdomains
... journals and subscription-based journals, con- cluding that there are no significant differences between them. Therefore, a model trained on one of these sources can be used successfully on the other, as long as the ... See full document
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Recognizing Salient Entities in Shopping Queries
... Xian-Sheng Hua, Linjun Yang, Jingdong Wang, Jing Wang, Ming Ye, Kuansan Wang, Yong Rui, and Jin Li. 2013. Clickage: Towards bridging semantic and intent gaps via mining click logs of search engines. In Proceedings ... See full document
5
Recognizing Complex Negation on Twitter
... on Twitter that actually hindered rescue ...for recognizing the negation of predicates on Twitter to find Japanese tweets that refute false ... See full document
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An Experimental Study of Hybrid Machine Learning Models for Extracting Named Entities
... Tables 1, 2, 3 show the results of Named Entity Recognition on three different language cor- pora with the Bi-LSTM-CRF and Gated-CNN-CRF models. One can notice that organization names are always the most difficult ... See full document
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Extracting psychiatric stressors for suicide from social media using deep learning
... GloVe Twitter em- bedding to initialize the embedding layer of CNN and compare the performance using dimensions at 50, 100, and 200 ...machine learning algorithms, including Extra Trees (ET), Random Forest ... See full document
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Annotating named entities in clinical text by combining pre annotation and active learning
... There is a corpus of Swedish clinical text, i.e. the text in the narrative part of the health record, that contains clinical text from the Stockholm area, from the years 2006-2008 (Dalianis et al., 2009). A subset of ... See full document
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Linking Named Entities to Any Database
... NED system used in production by a major Web company. This system is a modified version of the system described by Zhou et al. (2010), where cer- tain features have been removed for efficiency. We refer to this as the ... See full document
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Combining Spans into Entities: A Neural Two Stage Approach for Recognizing Discontiguous Entities
... The task of extracting overlapping entities has long been studied (Zhang et al., 2004; Zhou et al., 2004; Zhou, 2006; McDonald et al., 2005; Alex et al., 2007; Finkel and Manning, 2009; Lu and Roth, 2015; Muis and ... See full document
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