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 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 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

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

scholarly journals Smash++: an alignment-free and memory-efficient tool to find genomic rearrangements

GigaScience ◽  
2020 ◽  
Vol 9 (5) ◽  
Author(s):  
Morteza Hosseini ◽  
Diogo Pratas ◽  
Burkhard Morgenstern ◽  
Armando J Pinho
Keyword(s):  
Dna Sequences ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Genetic Disorders ◽  
Chromosomal Evolution ◽  
Genomic Rearrangements ◽  
Efficient Tool ◽  
Compression Technique ◽  
Alignment Free ◽  
Memory Efficient

Abstract Background The development of high-throughput sequencing technologies and, as its result, the production of huge volumes of genomic data, has accelerated biological and medical research and discovery. Study on genomic rearrangements is crucial owing to their role in chromosomal evolution, genetic disorders, and cancer. Results We present Smash++, an alignment-free and memory-efficient tool to find and visualize small- and large-scale genomic rearrangements between 2 DNA sequences. This computational solution extracts information contents of the 2 sequences, exploiting a data compression technique to find rearrangements. We also present Smash++ visualizer, a tool that allows the visualization of the detected rearrangements along with their self- and relative complexity, by generating an SVG (Scalable Vector Graphics) image. Conclusions Tested on several synthetic and real DNA sequences from bacteria, fungi, Aves, and Mammalia, the proposed tool was able to accurately find genomic rearrangements. The detected regions were in accordance with previous studies, which took alignment-based approaches or performed FISH (fluorescence in situ hybridization) analysis. The maximum peak memory usage among all experiments was ∼1 GB, which makes Smash++ feasible to run on present-day standard computers.

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.

scholarly journals Natural Language to SQL query Generation

2021 ◽  
Vol 9 (VI) ◽  
pp. 5069-5072
Author(s):  
Kiran Raj R
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Regular Expression ◽  
Parts Of Speech ◽  
Query Generation ◽  
Sql Query ◽  
Speech Tagging ◽  
The Web

Today, everyone has a personal device to access the web. Every user tries to access the knowledge that they require through internet. Most of the knowledge is within the sort of a database. A user with limited knowledge of database will have difficulty in accessing the data in the database. Hence, there’s a requirement for a system that permits the users to access the knowledge within the database. The proposed method is to develop a system where the input be a natural language and receive an SQL query which is used to access the database and retrieve the information with ease. Tokenization, parts-of-speech tagging, lemmatization, parsing and mapping are the steps involved in the process. The project proposed would give a view of using of Natural Language Processing (NLP) and mapping the query in accordance with regular expression in English language to SQL.

scholarly journals Verb Based Sentiment Research

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2468-2471
Keyword(s):  
Sentiment Analysis ◽  
Research Work ◽  
Online Reviews ◽  
Parts Of Speech ◽  
Pos Tagging ◽  
New Strategy ◽  
Semantic Orientation ◽  
The Difference ◽  
Pos Tagger ◽  
Speech Tagging

Sentiment Analysis is one of the leading research work. This paper proposes a model for the description of verbs that provide a structure for developing sentiment analysis. The verbs are very significant language elements and they receive the attention of linguistic researchers. The text is processed for parts-of-speech tagging (POS tagging). With the help of POS tagger, the verbs from each sentence are extracted to show the difference in sentiment analysis values. The work includes performing parts-of-speech tagging to obtain verb words and implement TextBlob and VADER to find the semantic orientation to mine the opinion from the movie review. We achieved interesting results, which were assessed effectively for accuracy by considering with and without verb form words. The findings show that concerning verb words accuracy increases along with emotion words. This introduces a new strategy to classify online reviews using components of algorithms for parts-of-speech..

Sign in / Sign up

Export Citation Format

Share Document