Parts of Speech Tagging for Indian Languages Review and Scope for Punjabi Language

2017 ◽  
Vol 7 (4) ◽  
pp. 214-217 ◽  
Author(s):  
Ramandeep Kaur ◽  
◽  
Lakhvir Singh Garcha ◽  
Mohita Garag ◽  
Satinderpal Singh ◽  
...  
Keyword(s):  
Indian Languages ◽  
Parts Of Speech ◽  
Speech Tagging

scholarly journals Abs-Sum-Kan: An Abstractive Text Summarization Technique for an Indian Regional Language by Induction of Tagging Rules

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1028-1036
Keyword(s):  
Named Entity Recognition ◽  
Qualitative Evaluation ◽  
Entity Recognition ◽  
Indian Languages ◽  
Parts Of Speech ◽  
Named Entity ◽  
Domain Specific ◽  
Full Abstraction ◽  
Regional Language ◽  
Speech Tagging

This paper presents a full abstraction for Indian languages, specifically Kannada, in the context of guided summarization. The proposed process generates the abstractive sum-mary by focusing on a unified presentation model with aspect based Information Extrac-tion (IE) rules and scheme based Templates. TF/IDF rules are used for classification into categories. Lexical analysis (like Parts Of Speech tagging and Named Entity Recognition) reduces prolixity, which leads to robust IE rules. Usage of Templates for sentence genera-tion makes the summaries succinct and information intensive. The IE rules are designed to accommodate the complexities of the considered languages. Later, the system aims to produce a guided summary of domain specific documents. An abstraction scheme is a collection of aspects and associated IE rules. Each abstraction scheme is designed based on a theme or subcategory. An extensive statistical and qualitative evaluation of the summaries generated by the system has been conducted and the results are found to be very promising.

A Survey on Parts of Speech Tagging for Indian Languages

2017 ◽  
Vol 7 (4) ◽  
pp. 209-213 ◽  
Author(s):  
Jagjeet Singh ◽  
◽  
Lakhvir Singh Garcha ◽  
Satinderpal Singh ◽  
◽  
...  
Keyword(s):  
Indian Languages ◽  
Parts Of Speech ◽  
Speech Tagging

scholarly journals Abs-Sum-Kan: An Abstractive Text Summarization Technique for an Indian Regional Language by Induction of Tagging Rules

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1225-1233
Keyword(s):  
Named Entity Recognition ◽  
Qualitative Evaluation ◽  
Entity Recognition ◽  
Indian Languages ◽  
Parts Of Speech ◽  
Named Entity ◽  
Domain Specific ◽  
Full Abstraction ◽  
Regional Language ◽  
Speech Tagging

This paper presents a full abstraction for Indian languages, specifically Kannada, in the context of guided summarization. The proposed process generates the abstractive summary by focusing on a unified presentation model with aspect based Information Extraction (IE) rules and scheme based Templates. TF/IDF rules are used for classification into categories. Lexical analysis (like Parts Of Speech tagging and Named Entity Recognition) reduces prolixity, which leads to robust IE rules. Usage of Templates for sentence generation makes the summaries succinct and information intensive. The IE rules are designed to accommodate the complexities of the considered languages. Later, the system aims to produce a guided summary of domain specific documents. An abstraction scheme is a collection of aspects and associated IE rules. Each abstraction scheme is designed based on a theme or subcategory. An extensive statistical and qualitative evaluation of the summaries generated by the system has been conducted and the results are found to be very promising.

scholarly journals Parts of Speech Tagging for Kannada

2019 ◽  
Author(s):  
Swaroop L R ◽  
◽  
Rakshit Gowda G S ◽  
Shriram Hegde ◽  
Sourabh U
Keyword(s):  
Parts Of Speech ◽  
Speech Tagging

A Memory-Efficient Tool for Bengali Parts of Speech Tagging

2020 ◽  
pp. 67-78
Author(s):  
Shadikun Nahar Sakiba ◽  
Md. Mahatab Uddin Shuvo ◽  
Najia Hossain ◽  
Samir Kumar Das ◽  
Joyita Das Mela ◽  
...  
Keyword(s):  
Efficient Tool ◽  
Parts Of Speech ◽  
Speech Tagging ◽  
Memory Efficient

Parts-of-Speech tagging for Malayalam using deep learning techniques

2020 ◽  
Vol 12 (3) ◽  
pp. 741-748 ◽  
Author(s):  
K. K. Akhil ◽  
R. Rajimol ◽  
V. S. Anoop
Keyword(s):  
Deep Learning ◽  
Parts Of Speech ◽  
Learning Techniques ◽  
Speech Tagging

Text Analysis of Assembly Work Instructions

2015 ◽  
Author(s):  
Rahul Sharan Renu ◽  
Gregory Mocko
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Lead Times ◽  
Parts Of Speech ◽  
Assembly Work ◽  
And Performance ◽  
Quality Of Products ◽  
Speech Tagging

The objective of this research is to investigate the requirements and performance of parts-of-speech tagging of assembly work instructions. Natural Language Processing of assembly work instructions is required to perform data mining with the objective of knowledge reuse. Assembly work instructions are key process engineering elements that allow for predictable assembly quality of products and predictable assembly lead times. Authoring of assembly work instructions is a subjective process. It has been observed that most assembly work instructions are not grammatically complete sentences. It is hypothesized that this can lead to false parts-of-speech tagging (by Natural Language Processing tools). To test this hypothesis, two parts-of-speech taggers are used to tag 500 assembly work instructions (obtained from the automotive industry). The first parts-of-speech tagger is obtained from Natural Language Processing Toolkit (nltk.org) and the second parts-of-speech tagger is obtained from Stanford Natural Language Processing Group (nlp.stanford.edu). For each of these taggers, two experiments are conducted. In the first experiment, the assembly work instructions are input to the each tagger in raw form. In the second experiment, the assembly work instructions are preprocessed to make them grammatically complete, and then input to the tagger. It is found that the Stanford Natural Language Processing tagger with the preprocessed assembly work instructions produced the least number of false parts-of-speech tags.

scholarly journals Multi Class Data Classification to Improve Accuracy in Sentiment Analysis using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 1457-1461
Author(s):  
Daram Vishnu
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Confusion Matrix ◽  
Training Data ◽  
Natural Languages ◽  
Parts Of Speech ◽  
Testing Data ◽  
Improve Accuracy ◽  
Textual Form ◽  
Speech Tagging

Sentiment analysis means classifying a text into different emotional classes. These days most of the sentiment analysis techniques divide the text into either binary or ternary classification in this paper we are classifying the movie reviews into 5 classes. Multi class sentiment analysis is a technique which can be used to know the exact sentiment of a review not just polarity of a given textual statement from positive to negative. So that one can know the precise sentiment of a review . Multi class sentiment analysis has always been a challenging task as natural languages are difficult to represent mathematically. The number of features are also generally large which requires huge computational power so to reduce the number of features we will use parts-of-speech tagging using textblob to extract the important features. Sentiment analysis is done using machine learning, where it requires training data and testing data to train a model. Various kinds of models are trained and tested at last one model is selected based on its accuracy and confusion matrix. It is important to analyze the reviews in textual form because large amount of reviews is present all over the web. Analyzing textual reviews can help the firms that are trying to find out the response of their products in the market. In this paper sentiment analysis is demonstrated by analyzing the movie reviews, reviews are taken from IMDB website.

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