scholarly journals Building Large Scale Text Corpus for Joint Word Segmentation and Part-of-Speech Tagging of Myanmar Language

2020 ◽  
Keyword(s):  
Large Scale ◽  
Word Segmentation ◽  
Text Corpus ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Speech Tagging

scholarly journals Aspect Based Sentiments from Tweets using Co-Ranking Multi-Modal Natural Language Processing Methodologies

2020 ◽  
Vol 8 (5) ◽  
pp. 1061-1068
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Detection System ◽  
Word Segmentation ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Sentiment Detection ◽  
Analysis System ◽  
Speech Tagging

Now-a-days people interest to spend their time in social sites especially twitters to post lot of tweets in every day. The posted tweets are used by many users to get the knowledge about the particular applications, products and other search engine queries. With the help of the posted tweets, their emotions and sentiments are derived which are used to get opinion about particular event. Lot of traditional sentiment detection system that has been developed but they failed to analyze huge volume of tweets and online contents with temporal patterns were also difficult to analyze. To overcome the above issues, the co-ranking multi-modal natural language processing based sentiment analysis system was developed to detect the emotions from the posted tweets. Initially, tweets of different events are collected from social sites which are processed by natural language procedures such as Stemming, Lemmatization, Part-of-speech tagging, word segmentation and parsing are applied to get the words related to posted tweets for deriving the sentiments. From the extracted emotions, co-ranking process is applied to get the opinion effectively related to particular event. Then the efficiency of the system is examined using experimental results and discussions. The introduced system recognize the sentiments from tweets with 98.80% of accuracy.

scholarly journals Improving Basic Natural Language Processing Tools for the Ainu Language

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 329
Author(s):  
Karol Nowakowski ◽  
Michal Ptaszynski ◽  
Fumito Masui ◽  
Yoshio Momouchi
Keyword(s):  
Language Processing ◽  
Positive Impact ◽  
Word Segmentation ◽  
Data Set ◽  
Part Of Speech Tagging ◽  
Endangered Language ◽  
Part Of Speech ◽  
Overall Performance ◽  
Speech Tagging ◽  
Northern Japan

Ainu is a critically endangered language spoken by the native inhabitants of northern Japan. This paper describes our research aimed at the development of technology for automatic processing of text in Ainu. In particular, we improved the existing tools for normalizing old transcriptions, word segmentation, and part-of-speech tagging. In the experiments we applied two Ainu language dictionaries from different domains (literary and colloquial) and created a new data set by combining them. The experiments revealed that expanding the lexicon had a positive impact on the overall performance of our tools, especially with test data unrelated to any of the training sets used.

scholarly journals A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging

2017 ◽  
Author(s):  
Xinchi Chen ◽  
Xipeng Qiu ◽  
Xuanjing Huang
Keyword(s):  
Language Processing ◽  
Neural Model ◽  
Word Segmentation ◽  
Chinese Word ◽  
Chinese Word Segmentation ◽  
Neural Network Models ◽  
Long Distance ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Speech Tagging

Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering. However, the previous neural models cannot extract the complicated feature compositions as the traditional methods with discrete features. In this work, we propose a feature-enriched neural model for joint Chinese word segmentation and part-of-speech tagging task. Specifically, to simulate the feature templates of traditional discrete feature based models, we use different filters to model the complex compositional features with convolutional and pooling layer, and then utilize long distance dependency information with recurrent layer. Experimental results on five different datasets show the effectiveness of our proposed model.

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