scholarly journals Natural Language Querying and Visualization System

2021 ◽  
Author(s):  
Trishali Banerjee ◽  
Upasana Bhattacharjee ◽  
K. R. Jansi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Technical Skills ◽  
Data Driven ◽  
Visualization System ◽  
The People ◽  
Two Systems ◽  
Data Visualizations

Data is the new gold; everything is data driven. But it is impossible for everyone to possess technical skills to be able to write queries and know different python tools used for data visualizations. The process of extracting information from a database is a mammoth task for non-technical users as it requires one to have extensive knowledge of DBMS language. But these data and visualizations are required for various everyday presentations and interactions in the professional world. This application would enable the users to overcome these obstacles. Our project aims at integrating two systems, an NLP interface to fetch data from simple English queries, and a second system where the fetched data with the help of natural language processing is used to form visualizations as demanded by the users will be created. This system would essentially help the people who are not techno-savvy or are not in the field of tech to interact with data using simple English.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

scholarly journals Natural Language Processing Based Method for Clustering and Analysis of Aviation Safety Narratives

Aerospace ◽  
2020 ◽  
Vol 7 (10) ◽  
pp. 143
Author(s):  
Rodrigo L. Rose ◽  
Tejas G. Puranik ◽  
Dimitri N. Mavris
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Safety Data ◽  
Aviation Safety ◽  
Data Driven ◽  
Flight Safety ◽  
Commercial Aviation ◽  
Unique Challenge ◽  
Aviation Safety Reporting System

The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown in popularity. Data-driven techniques offer efficient and repeatable exploration of patterns and anomalies in large datasets. Text-based flight safety data presents a unique challenge in its subjectivity, and relies on natural language processing tools to extract underlying trends from narratives. In this paper, a methodology is presented for the analysis of aviation safety narratives based on text-based accounts of in-flight events and categorical metadata parameters which accompany them. An extensive pre-processing routine is presented, including a comparison between numeric models of textual representation for the purposes of document classification. A framework for categorizing and visualizing narratives is presented through a combination of k-means clustering and 2-D mapping with t-Distributed Stochastic Neighbor Embedding (t-SNE). A cluster post-processing routine is developed for identifying driving factors in each cluster and building a hierarchical structure of cluster and sub-cluster labels. The Aviation Safety Reporting System (ASRS), which includes over a million de-identified voluntarily submitted reports describing aviation safety incidents for commercial flights, is analyzed as a case study for the methodology. The method results in the identification of 10 major clusters and a total of 31 sub-clusters. The identified groupings are post-processed through metadata-based statistical analysis of the learned clusters. The developed method shows promise in uncovering trends from clusters that are not evident in existing anomaly labels in the data and offers a new tool for obtaining insights from text-based safety data that complement existing approaches.

scholarly journals Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods

2010 ◽  
Vol 36 (3) ◽  
pp. 341-387 ◽  
Author(s):  
Nitin Madnani ◽  
Bonnie J. Dorr
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Recent Work ◽  
Language Processing ◽  
Data Driven ◽  
Future Trends ◽  
Automatic Construction ◽  
Work Done ◽  
Paraphrase Generation ◽  
Potential Use

The task of paraphrasing is inherently familiar to speakers of all languages. Moreover, the task of automatically generating or extracting semantic equivalences for the various units of language—words, phrases, and sentences—is an important part of natural language processing (NLP) and is being increasingly employed to improve the performance of several NLP applications. In this article, we attempt to conduct a comprehensive and application-independent survey of data-driven phrasal and sentential paraphrase generation methods, while also conveying an appreciation for the importance and potential use of paraphrases in the field of NLP research. Recent work done in manual and automatic construction of paraphrase corpora is also examined. We also discuss the strategies used for evaluating paraphrase generation techniques and briefly explore some future trends in paraphrase generation.

scholarly journals Data-driven materials research enabled by natural language processing and information extraction

2020 ◽  
Vol 7 (4) ◽  
pp. 041317
Author(s):  
Elsa A. Olivetti ◽  
Jacqueline M. Cole ◽  
Edward Kim ◽  
Olga Kononova ◽  
Gerbrand Ceder ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Data Driven ◽  
Materials Research

Development of Natural Language Processing based Communication and Educational Assisted Systems for the People with Hearing Disability in Myanmar

2019 ◽  
Vol 20 (48) ◽  
Author(s):  
Swe Zin Moe ◽  
Ye Kyaw Thu ◽  
Hlaing Myat Nwe ◽  
Hnin Wai Wai Hlaing ◽  
Ni Htwe Aung ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hearing Disability ◽  
The People

scholarly journals Desktop Voice Assistant Using Natural Language Processing (NLP)

2020 ◽  
Vol 6 (12) ◽  
pp. 332-335
Author(s):  
Lalit Kumar
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cloud Services ◽  
Home Automation ◽  
The People ◽  
The Future ◽  
The Voice ◽  
The Way

Voice assistants are the great innovation in the field of AI that can change the way of living of the people in a different manner. the voice assistant was first introduced on smartphones and after the popularity it got. It was widely accepted by all. Initially, the voice assistant was mostly being used in smartphones and laptops but now it is also coming as home automation and smart speakers. Many devices are becoming smarter in their own way to interact with human in an easy language. The Desktop based voice assistant are the programs that can recognize human voices and can respond via integrated voice system. This paper will define the working of a voice assistants, their main problems and limitations. In this paper it is described that the method of creating a voice assistant without using cloud services, which will allow the expansion of such devices in the future.

scholarly journals CAREER RECOMMENDER: A NOVEL APPROACH TO SUGGEST JOBS AND POST-GRADUATION STREAMS

2017 ◽  
Vol 10 (13) ◽  
pp. 365
Author(s):  
Prafful Nath Mathur ◽  
Abhishek Dixit ◽  
Sakkaravarthi Ramanathan
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Technical Skills ◽  
Web Crawler ◽  
Novel Approach ◽  
Current Location ◽  
Job Postings ◽  
Mining Algorithms

To implement a novel approach to recommend jobs and colleges based on résumé of freshly graduated students. Job postings are crawled from web using a web crawler and stored in a customized database. College lists are also retrieved for post-graduation streams and stored in a database. Student résumé is stored and parsed using natural language processing methods to form a résumé model. Text mining algorithms are applied on this model to extract useful information (i.e., degree, technical skills, extracurricular skills, current location, and hobbies). This information is used to suggest matching jobs and colleges to the candidate. 

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