Robust multilingual Named Entity Recognition with shallow semi-supervised features
Rodrigo Agerri,German Rigau +1 more
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TLDR
This work presents a multilingual Named Entity Recognition approach based on a robust and general set of features across languages and datasets that combines shallow local information with clustering semi-supervised features induced on large amounts of unlabeled text.About:
This article is published in Artificial Intelligence.The article was published on 2016-09-01 and is currently open access. It has received 78 citations till now. The article focuses on the topics: Cluster analysis & Named-entity recognition.read more
Citations
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Proceedings Article
A Survey on Recent Advances in Named Entity Recognition from Deep Learning models
Vikas Yadav,Steven Bethard +1 more
TL;DR: This work presents a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms.
Journal ArticleDOI
W2VLDA: Almost unsupervised system for Aspect Based Sentiment Analysis
TL;DR: W2VLDA as discussed by the authors is an almost unsupervised system based on topic modeling that, combined with some other un-supervised methods and a minimal configuration step, performs aspect category classification, aspect-term and opinion-word separation and sentiment polarity classification for any given domain and language.
Journal ArticleDOI
Named Entity Extraction for Knowledge Graphs: A Literature Overview
TL;DR: The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL), and observes that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions.
Posted Content
W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis
TL;DR: W2VLDA is described, an almost unsupervised system based on topic modelling, that combined with some other unsuper supervised methods and a minimal configuration, performs aspect/category classifiation, aspect-terms/opinion-words separation and sentiment polarity classification for any given domain and language.
Proceedings Article
Give your Text Representation Models some Love: the Case for Basque
Rodrigo Agerri,Iñaki San Vicente,Jon Ander Campos,Ander Barrena,Xabier Saralegi,Aitor Soroa,Eneko Agirre +6 more
TL;DR: A number of monolingual models (FastText word embeddings, FLAIR and BERT language models) trained with larger Basque corpora produce much better results than publicly available versions in downstream NLP tasks, including topic classification, sentiment classification, PoS tagging and NER.
References
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Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Proceedings ArticleDOI
Moses: Open Source Toolkit for Statistical Machine Translation
Philipp Koehn,Hieu Hoang,Alexandra Birch,Chris Callison-Burch,Marcello Federico,Nicola Bertoldi,Brooke Cowan,Wade Shen,C. Corbett Moran,Richard Zens,Chris Dyer,Ondrej Bojar,Alexandra Elena Constantin,Evan Herbst +13 more
TL;DR: An open-source toolkit for statistical machine translation whose novel contributions are support for linguistically motivated factors, confusion network decoding, and efficient data formats for translation models and language models.
Proceedings ArticleDOI
A unified architecture for natural language processing: deep neural networks with multitask learning
Ronan Collobert,Jason Weston +1 more
TL;DR: This work describes a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense using a language model.
Book
Sentiment Analysis and Opinion Mining
TL;DR: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language as discussed by the authors and is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining.
Proceedings ArticleDOI
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
TL;DR: The CoNLL-2003 shared task on NER as mentioned in this paper was the first NER task with language-independent named entity recognition (NER) data sets and evaluation method, and a general overview of the systems that participated in the task and their performance.