Nested Named Entity Recognition Revisited
Published 1 January 2018Open access
Arzoo Katiyar, Claire Cardie
Citations252
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection that significantly outperforms existing state-of-the-art methods, which are feature-based.
Abstract
Arzoo Katiyar, Claire Cardie. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
Keywords
Computer ScienceDecision Sciences
Dropout: a simple way to prevent neural networks from overfitting
34,279 Citations2014Nitish Srivastava, Geoffrey E. Hinton +3 more
It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
arXiv (Cornell University)Distributed Representations of Words and Phrases and their Compositionality
18,086 Citations2013Tomáš Mikolov, Ilya Sutskever +3 more
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.
arXiv (Cornell University)Sequence to Sequence Learning with Neural Networks
13,362 Citations2014Ilya Sutskever, Oriol Vinyals +1 more
ScholarlyCommons (University of Pennsylvania)Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
12,978 Citations2001John Lafferty, Andrew McCallum +1 more
This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
arXiv (Cornell University)ADADELTA: An Adaptive Learning Rate Method
5,531 Citations2012Matthew D. Zeiler
A novel per-dimension learning rate method for gradient descent called ADADELTA that dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent is presented.
Neural Architectures for Named Entity Recognition
4,428 Citations2016Guillaume Lample, Miguel Ballesteros +3 more
Comunicacio presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 of juny 2016.
Distant supervision for relation extraction without labeled data
2,913 Citations2009Mike D. Mintz, Steven Bills +2 more
This work investigates an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corpora of any size.
Lingvisticae InvestigationesA survey of named entity recognition and classification
2,488 Citations2007David R. Nadeau, Satoshi Sekine
Observations about languages, named entity types, domains and textual genres studied in the literature, along with other critical aspects of NERC such as features and evaluation methods, are reported.
Named entity recognition using an HMM-based chunk tagger
718 Citations2001Guodong Zhou, Jian Su
A Hidden Markov Model and an HMM-based chunk tagger is proposed, from which a named entity (NE) recognition system is built to recognize and classify names, times and numerical quantities, and the NER problem can be resolved effectively.
Semi-Markov Conditional Random Fields for Information Extraction
617 Citations2004Sunita Sarawagi, William W. Cohen
Intuitively, a semi-CRF on an input sequence x outputs a "segmentation" of x, in which labels are assigned to segments rather than to individual elements of xi, and transitions within a segment can be non-Markovian.
Research Portal (King's College London)Distributional Semantics Resources for Biomedical Text Processing
441 Citations2013Sampo Pyysalo, Filip Ginter +3 more
This study introduces the first set of such language resources created from analysis of the entire available biomedical literature, including a dataset of all 1to 5-grams and their probabilities in these texts and new models of word semantics.
arXiv (Cornell University)Grammar as a Foreign Language
403 Citations2014Oriol Vinyals, Łukasz Kaiser +4 more
Nested named entity recognition
322 Citations2009Jenny Rose Finkel, Christopher D. Manning
This paper presents a new technique for recognizing nested named entities, by using a discriminative constituency parser, which outperforms a standard semi-CRF on the more traditional top-level entities.
International Conference on Machine LearningFrom softmax to sparsemax: a sparse model of attention and multi-label classification
294 Citations2016André F. T. Martins, Ramón Fernández Astudillo
Joint Entity Recognition and Disambiguation
265 Citations2015Gang Luo, Xiaojiang Huang +2 more
JERL is the first model to jointly optimize NER and linking tasks together completely, and in experiments on the CoNLL’03/AIDA data set, JERL outperforms state-of-art NER and linking systems.
arXiv (Cornell University)From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
262 Citations2016André F. T. Martins, Ramón Fernández Astudillo
Sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities, is proposed, and an unexpected connection between this new loss and the Huber classification loss is revealed.
The GENIA corpus: an annotated research abstract corpus in molecular biology domain
259 Citations2002Tomoko Ohta, Yuka Tateisi +1 more
Joint Mention Extraction and Classification with Mention Hypergraphs
230 Citations2015Wei Lu, Dan Roth
A novel model is presented for the task of joint mention extraction and classification that is able to effectively capture overlapping mentions with unbounded lengths and can be extended to additionally capture mention heads explicitly in a joint manner under the same time complexity.
BMC BioinformaticsIdentifying gene and protein mentions in text using conditional random fields
223 Citations2005Ryan McDonald, Fernando Pereira
A diverse feature set containing standard orthographic features combined with expert features in the form of gene and biological term lexicons is employed to achieve a precision of 86.4% and recall of 78.7% for tagging gene and protein mentions from text using the probabilistic sequence tagging framework of conditional random fields.
Joint extraction of entities and relations for opinion recognition
213 Citations2006Yejin Choi, Eric Breck +1 more
An integer linear programming approach is employed to solve the joint opinion recognition task, and it is shown that global, constraint-based inference can significantly boost the performance of both relation extraction and the extraction of opinion-related entities.
A Statistical Model for Multilingual Entity Detection and Tracking
187 Citations2004Radu Florian, H. Hassan +6 more
This paper presents a statistical language-independent framework for identifying and tracking named, nominal and pronominal references to entities within unrestricted text documents, and chaining them into clusters corresponding to each logical entity present in the text.
Recognising nested named entities in biomedical text
159 Citations2007Beatrice Alex, Barry Haddow +1 more
This paper introduces and compares three techniques for modelling and recognising nested entities by means of a conventional sequence tagger and yields an improvement over the baseline tagger that is only trained on flat annotation.
Investigating LSTMs for Joint Extraction of Opinion Entities and Relations
144 Citations2016Arzoo Katiyar, Claire Cardie
It is found that standard LSTMs are not competitive with a state-of-the-art CRF+ILP joint inference approach to opinion entities extraction, performing below even the standalone sequence-tagging CRF.
Parsing and Hypergraphs
93 Citations2001Dan Klein, Christopher D. Manning
A Constrained Latent Variable Model for Coreference Resolution
58 Citations2013Kai-Wei Chang, Rajhans Samdani +1 more
The Latent Left Linking model (L 3 M), a novel, principled, and linguistically motivated latent structured prediction approach to coreference resolution, is described and it is shown that L 3 M admits efficient inference and can be augmented with knowledge-based constraints.
Text, speech and language technologyParsing and Hypergraphs
51 Citations2004Dan Klein, Christopher D. Manning
This work illustrates the approach by showing how a dynamic extension of Dijkstra's algorithm can be used to construct a probabilistic chart parser with an O(n3) time bound for arbitrary PCFGs, while preserving as much of the flexibility of symbolic chart parsers as is allowed by the inherent ordering of Probabilistic dependencies.