Multi-language named-entity recognition system based on HMM

Saito, Kuniko; Nagata, Michio

doi:10.3115/1119384.1119390

Cited by 14 publications

(2 citation statements)

References 11 publications

(5 reference statements)

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“…in given text. Various methods have been proposed to tackle this problem, including Hidden Markov Models(HMMs) (Saito and Nagata, 2003), Maximum Entropy Models(ME) (Chieu and Ng, 2003), Support Vector Machines(SVM) (Ekbal and Bandyopadhyay, 2010) and Conditional Random Fields(CRF) (Feng et al, 2006). With the development of deep learning, neural net- → "水(Water)", the model incorrectly predicts that "江(River)" and "水(Water)" belong to the same entity.…”

Section: Introductionmentioning

confidence: 99%

An Encoding Strategy Based Word-Character

Liu¹,

Tongge²,

Xu³

et al. 2019

Proceedings of the 2019 Conference of the North

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A recently proposed lattice model has demonstrated that words in character sequence can provide rich word boundary information for character-based Chinese NER model. In this model, word information is integrated into a shortcut path between the start and the end characters of the word. However, the existence of shortcut path may cause the model to degenerate into a partial word-based model, which will suffer from word segmentation errors. Furthermore, the lattice model can not be trained in batches due to its DAG structure. In this paper, we propose a novel wordcharacter LSTM(WC-LSTM) model to add word information into the start or the end character of the word, alleviating the influence of word segmentation errors while obtaining the word boundary information. Four different strategies are explored in our model to encode word information into a fixed-sized representation for efficient batch training. Experiments on benchmark datasets show that our proposed model outperforms other stateof-the-arts models.

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Section: Introductionmentioning

confidence: 99%

An Encoding Strategy Based Word-Character

Liu¹,

Tongge²,

Xu³

et al. 2019

Proceedings of the 2019 Conference of the North

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“…The study by [24] produced a system capable of performing multi-language named-entity detection using HMM-based approach.…”

Section: Indonesian Natural Language Processing Toolmentioning

confidence: 99%

Opinion sentence classification in Indonesian review document

Panggabean

Purwarianti

2012

2012 7th International Conference on Telecommunication Systems, Services, and Applications (TSSA)

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Opinion sentence classification in Indonesian review document has never been done yet, considering special characteristics of review document that we found in this research. Among others are the sentence type in the review document, complex sentence structure, non standard sentence pattern, non formal vocabulary, excessive punctuation usage and many mixing language. The aim of this research is to provide necessary process for opinion sentence classification in an Indonesia language review document regardless of only few low accuracy NLP (Natural Language Processing) tools available for Indonesian language. Opinion sentence classification in this research is done with sentiment and subject detection approach in sentence. By locating sentiment word in sentence and determining whether the clause subject is considered as the frequent subject which is often subjected to an opinion, one could determine whether the sentence is an opinion expression or not. The proposed opinion sentence classification require two processes: the subject accumulation to accumulate relevant subject words automatically; and sentiment classification to classify sentence as opinion sentence or not. Accuracy of the experiment (using precision score) of the method proposed on the opinion sentence classification is up to 87%.

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A Text Annotation Tool with Pre-annotation Based on Deep Learning

Teng

et al. 2019

Lecture Notes in Computer Science

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Multi-language named-entity recognition system based on HMM

Cited by 14 publications

References 11 publications

An Encoding Strategy Based Word-Character

An Encoding Strategy Based Word-Character

Opinion sentence classification in Indonesian review document

A Text Annotation Tool with Pre-annotation Based on Deep Learning

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