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A Character String-Based Stemming for Morphologically Derivative Languages
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In: Information; Volume 13; Issue 4; Pages: 170 (2022)
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Research on Named Entity Recognition Methods in Chinese Forest Disease Texts
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In: Applied Sciences; Volume 12; Issue 8; Pages: 3885 (2022)
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Leveraging Part-of-Speech Tagging Features and a Novel Regularization Strategy for Chinese Medical Named Entity Recognition
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In: Mathematics; Volume 10; Issue 9; Pages: 1386 (2022)
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| 4 |
Korean Prosody Phrase Boundary Prediction Model for Speech Synthesis Service in Smart Healthcare
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In: Electronics ; Volume 10 ; Issue 19 (2021)
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| 5 |
The Comparison Between the Tools for Named Entity Recognition
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| 7 |
Automatic Period Segmentation of Oral French ; Segmentation automatique du français parlé en périodes macrosyntaxiques
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In: 12th International Conference on Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-02770725 ; 12th International Conference on Language Resources and Evaluation, May 2020, Marseille, France (2020)
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| 8 |
Segmentation automatique en périodes pour le français parlé
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In: Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 2 : Traitement Automatique des Langues Naturelles ; 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 2 : Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-02784773 ; 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 2 : Traitement Automatique des Langues Naturelles, 2020, Nancy, France. pp.241-248 (2020)
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| 9 |
Chunking different spoken speech types : challenges for machine learning ; Chunker différents types de discours oraux : défis pour l'apprentissage automatique
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In: Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) - PFIA 2019 - Volume II : Articles courts ; https://hal.archives-ouvertes.fr/hal-02567769 ; Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) - PFIA 2019 - Volume II : Articles courts, Jul 2019, Toulouse, France. pp.195-204 (2019)
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| 10 |
An Improved Word Representation for Deep Learning Based NER in Indian Languages
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In: Information ; Volume 10 ; Issue 6 (2019)
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| 11 |
Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF
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In: Electronics ; Volume 8 ; Issue 11 (2019)
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| 12 |
Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation
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In: Sensors ; Volume 19 ; Issue 7 (2019)
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| 13 |
Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
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In: Future Internet ; Volume 11 ; Issue 1 (2019)
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| 14 |
Dialog Acts Annotations for Online Chats ; Annotation en Actes de Dialogue pour les Conversations d’Assistance en Ligne
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In: Actes TALN-RECITAL 2018 ; 25e conférence sur le Traitement Automatique des Langues Naturelles (TALN) ; https://hal.archives-ouvertes.fr/hal-01943345 ; 25e conférence sur le Traitement Automatique des Langues Naturelles (TALN), 2018, Rennes, France (2018)
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| 15 |
Generative vs. Discriminative Recognition Models for Off-Line Arabic Handwriting
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In: Sensors ; Volume 18 ; Issue 9 (2018)
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| 16 |
A Multitask-Based Neural Machine Translation Model with Part-of-Speech Tags Integration for Arabic Dialects
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In: Applied Sciences ; Volume 8 ; Issue 12 (2018)
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| 17 |
Aspect Term Extraction Based on MFE-CRF
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In: Information ; Volume 9 ; Issue 8 (2018)
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| 18 |
Automatic Extraction of TEI Structures in Digitized Lexical Resources using Conditional Random Fields
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In: electronic lexicography, eLex 2017 ; https://hal.archives-ouvertes.fr/hal-01508868 ; electronic lexicography, eLex 2017, Sep 2017, Leiden, Netherlands (2017)
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| 19 |
Structured Named Entity Recognition by Cascading CRFs
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In: Intelligent Text Processing and Computational Linguistics (CICling) ; https://hal.archives-ouvertes.fr/hal-01579109 ; Intelligent Text Processing and Computational Linguistics (CICling), Apr 2017, Budapest, Hungary (2017)
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Abstract:
International audience ; NER is an important task in NLP, often used as a basis for further treatments. A new challenge has emerged in the last few years: structured named entity recognition, where not only named entities must be identied but also their hierarchical components. In this article, we describe a cascading CRFs approach to address this challenge. It reaches the state of the art while remaining very simple on a structured NER challenge. We then oer an error analysis of our system based on a detailed, yet simple, error classication. 1 Introduction In this paper, we present a linear CRF cascade approach for structured named entity recognition (SNER) on Quaero v1 and v2 corpora, used in the ETAPE evaluation campaigns [10]. Named Entity Recognition (NER) is a fundamental NLP task, its structured variant being increasingly popular. We can overall distinguish two main approaches used to address this task, the rst one being cascading multiple annotations with either the same or dierent methods. In this respect, we can cite [19], which cascaded rules in order to gradually build the structure. We can also cite [5], where a CRF and a PCFG were used, the former giving the leaves while the latter built the rest of the tree. And nally [22], the winner of ETAPE, used one CRF per entity type, for a total of 68 CRFs, and then aligned their annotations. The second approach to annotate tree-structured named entities is to directly retrieve the structure, as was done by [20], who used partial annotation rules for predicting beginnings and ends of entities and then built the tree in one pass. Finally, we can cite [8], who used a tree-CRF to learn nested biomedical entities on the GENIA corpus [14]. Cascading linear CRFs have also been applied for syntactic parsing, as did [25]. At each step, they retrieved chunks and then only kept their respective heads for the next iteration until only one chunk covering the whole sentence was found (with the class sentence). The tree was then reconstructed by simply unfolding chunks at each step. In this paper, we design a new, more general and eective cascade of CRFs adapted to the ETAPE evaluation campaign (sections 2 and 3), evaluate its eciency and analyse its errors (section 4) and nally conclude (section 5).
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Keyword:
[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; CRF; machine learning; Quaero; structured named entity recognition
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URL: https://hal.archives-ouvertes.fr/hal-01579109/document
https://hal.archives-ouvertes.fr/hal-01579109/file/2017_CICling_CRFCascade.pdf
https://hal.archives-ouvertes.fr/hal-01579109
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| 20 |
3D Abstract Scene Synthesis from Sentences
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Shao, Jingyu. - : eScholarship, University of California, 2017
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In: Shao, Jingyu. (2017). 3D Abstract Scene Synthesis from Sentences. UCLA: Statistics 0891. Retrieved from: http://www.escholarship.org/uc/item/86m41514 (2017)
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