Hidden Markov model for POS tagging in word sense disambiguation

Alva, Pooja; Hegde, Vinay

doi:10.1109/csitss.2016.7779371

Cited by 11 publications

(3 citation statements)

References 5 publications

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“…Hidden Markov Model can help to find the probability of certain words and to predict the probability of remaining words in the sequence [9]. There are three processes Initialization which means getting the number of word labels, Transition of label search after the label is checked, and Emission of the number of words from the label from the training data.…”

Section: Hidden Markov Modelmentioning

confidence: 99%

POS Tagger Improvisation with the Addition of Foreign Word Labels on Telkom University News

Setyono

Richasdy

Purbolaksono

2022

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News is a medium of daily information usually obtained by the public. The news consists of a lot of information in it and is composed of sentence structures. Each language is unique with its own sentence structure, like Indonesian and other foreign languages. But nowadays, many media mix Indonesian with foreign languages, making the sentence structure different from Bahasa Indonesia. To classify these words, Part Of Speech Tagging needed to determine the class of words composed of sentences by learning from the Corpus of each language. With the new sentence structure, POS Tagger requires a larger Corpus to learn. The language structure can determine the results of tagging from the POS Tagger. If there are words that are not in the Corpus, it can reduce the accuracy of the POS Tagger. We conducted to enhance the research results by adding data with a different sentence structure from the Indonesian Language Corpus using sentences from online media. Added about 242 sentences with 7,043 tokens on Corpus focused on Foreign Word tags, which total 3819 tags. After doing some testing and scenarios, the results of the accuracy of POS Tagger show an accuracy of 94.7% using the Hidden Markov Model method with the F1-Score tag FW 78%.

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Section: Hidden Markov Modelmentioning

confidence: 99%

POS Tagger Improvisation with the Addition of Foreign Word Labels on Telkom University News

Setyono

Richasdy

Purbolaksono

2022

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“…Part-of-Speech (POS) information is important for disambiguating words with multiple meanings (Alva and Hegde 2016). This is because an ambiguous word carries a specific POS in a particular context (Pal, Munshi, and Saha 2015).…”

Section: Encoding Part-of-speech Knowledgementioning

confidence: 99%

Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling

Wang

Wei

Radfar

et al. 2021

AAAI

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We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling. Specifically, we encode syntactic knowledge into the Transformer encoder by jointly training it to predict syntactic parse ancestors and part-of-speech of each token via multi-task learning. Our model is based on self-attention and feed-forward layers and does not require external syntactic information to be available at inference time. Experiments show that on two benchmark datasets, our models with only two Transformer encoder layers achieve state-of-the-art results. Compared to the previously best performed model without pre-training, our models achieve absolute F1 score and accuracy improvement of 1.59 % and 0.85 % for slot filling and intent detection on the SNIPS dataset, respectively. Our models also achieve absolute F1 score and accuracy improvement of 0.1 % and 0.34 % for slot filling and intent detection on the ATIS dataset, respectively, over the previously best performed model. Furthermore, the visualization of the self-attention weights illustrates the benefits of incorporating syntactic information during training.

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“…It is an upstream task that preprocesses the input texts to assist more complex NLP applications. Since the POS of a word can affect its meaning and polarity, POS tagging is important for downstream tasks, e.g., word sense disambiguation [12,343], information retrieval [201], sentiment analysis [333] T-News 76% F1 Table 2.4: Widely used corpora for POS tagging. [18,235], cyber-bullying detection [240], dialogue systems [23,223], metaphor detection [118,207] and interpretation [206,208].…”

Section: Pos Taggingmentioning

confidence: 99%

Disentangling syntactics, semantics, and pragmatics in natural language processing

Zhang

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It has been my utmost privilege to be given this opportunity to pursue a Ph.D degree.First, I would like to express my sincere gratitude to my supervisor, Prof. Erik Cambria for his support and guidance throughout my Ph.D candidature. It is with his warm encouragement that I found the courage and confidence to pursue a Ph.D degree.Next, I would like to thank my co-supervisor, Dr Rui Mao, for his patient guidance.His insights and advice played a vital role in orientating my research direction and raising the quality of my works. I deeply appreciate his efforts in helping me with my Ph.D project.I would also like to thank my fellow Sentic Team members Mengshi Ge, Luyao Zhu, Jinjie Ni, Zonglin Yang, Kai He, Tom Yang, etc., for their friendship.Last but not the least, I owe enormous gratitude to my parents. The achievements in this thesis would not have come to be if not for their love, care, and encouragement. Special thanks goes to my long-time friends Wenrui Li, Qiyu Zhong, and Zhixin Xu, who lifted my spirit in times of stress and anxiety with compassion and killer memes.

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Hidden Markov model for POS tagging in word sense disambiguation

Cited by 11 publications

References 5 publications

POS Tagger Improvisation with the Addition of Foreign Word Labels on Telkom University News

POS Tagger Improvisation with the Addition of Foreign Word Labels on Telkom University News

Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling

Disentangling syntactics, semantics, and pragmatics in natural language processing

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