scholarly journals Sentence Extraction for Machine Comprehension

2019 ◽  
Vol 8 (2) ◽  
pp. 5511-5514
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Research Area ◽  
Performance Comparison ◽  
Word Count ◽  
Natural Language Text ◽  
Sentence Extraction ◽  
The Given ◽  
Language Text

Machine comprehension is a broad research area from Natural Language Processing domain, which deals with making a computerised system understand the given natural language text. Question answering system is one such variant used to find the correct ‘answer’ for a ‘query’ using the supplied ‘context’. Using a sentence instead of the whole context paragraph to determine the ‘answer’ is quite useful in terms of computation as well as accuracy. Sentence selection can, therefore, be considered as a first step to get the answer. This work devises a method for sentence selection that uses cosine similarity and common word count between each sentence of context and question. This removes the extensive training overhead associated with other available approaches, while still giving comparable results. The SQuAD dataset is used for accuracy based performance comparison.

scholarly journals Event Time Relationship in Natural Language Text

2019 ◽  
Vol 7 (3) ◽  
pp. 4
Author(s):  
Vanitha Guda ◽  
SureshKumar Sanampudi
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Information Needs ◽  
Question Answering ◽  
Data Set ◽  
Natural Language Text ◽  
Event Time ◽  
The Given ◽  
Events Data ◽  
Language Text

<p>Due to the numerous information needs, retrieval of events from a given natural language text is inevitable. In natural language processing (NLP) perspective, "Events" are situations, occurrences, real-world entities or facts. Extraction of events and arranging them on a timeline is helpful in various NLP application like building the summary of news articles, processing health records, and Question Answering System (QA) systems. This paper presents a framework for identifying the events and times from a given document and representing them using a graph data structure.  As a result, a graph is derived to show event-time relationships in the given text. Events form the nodes in a graph, and edges represent the temporal relations among the nodes. Time of an event occurrence exists in two forms namely qualitative (like before, after, duringetc) and quantitative (exact time points/periods). To build the event-time-event structure quantitative time is normalized to qualitative form. Thus obtained temporal information is used to label the edges among the events. Data set released in the shared task EvTExtract of (Forum for Information Retrieval Extraction) FIRE 2018 conference is identified to evaluate the framework. Precision and recall are used as evaluation metrics to access the performance of the proposed framework with other methods mentioned in state of the art with 85% of accuracy and 90% of precision.</p>

scholarly journals Event -Time Relation in Natural Language Text

2019 ◽  
Vol 8 (6) ◽  
pp. 716-724
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Information Needs ◽  
Question Answering ◽  
Data Set ◽  
Natural Language Text ◽  
Event Time ◽  
The Given ◽  
Events Data ◽  
Language Text

Due to the numerous information needs, retrieval of events from a given natural language text is inevitable. In natural language processing(NLP), "Events" are situations, occurrences, real-world entities or facts. Extraction of events and arranging them on a timeline is helpful in various NLP applications like building the summary of news articles, processing health records, and Question Answering System (QA) systems. This paper presents a framework for identifying the events and times from a given document and representing them using a graph data structure. As a result, a graph is derived to show event-time relationships in the given text. Events form the nodes in a graph, and edges represent the temporal relations among the nodes. Time of an event occurrence exists in two forms namely qualitative (like before, after, during, etc.) and quantitative (exact time points/periods). To build the event-time-event structure quantitative time is normalized to qualitative form. Thus obtained temporal information is used to label the edges among the events. Data set released in the shared task EvTExtract of (Forum for Information Retrieval Extraction) FIRE 2018 conference is identified to evaluate the framework. Precision and recall are used as evaluation metrics to access the performance of the work with other methods mentioned in state of the art with 85% of accuracy and 90% of precision.

scholarly journals AN AUTOMATIC WEB-BASED QUESTION ANSWERING SYSTEM FOR E-LEARNING

2017 ◽  
Vol 58 (2) ◽  
pp. 1
Author(s):  
Waheeb Ahmed ◽  
Babu Anto
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Web Based ◽  
Question Answering System ◽  
Natural Language Text ◽  
Exact Answer ◽  
E Learning ◽  
Language Text ◽  
Develop Technique

An automatic web based Question Answering (QA) system is a valuable tool for improving e-learning and education. Several approaches employ natural language processing technology to understand questions given in natural language text, which is incomplete and error-prone. In addition, instead of extracting exact answer, many approaches simply return hyperlinks to documents containing the answers, which is inconvenient for the students or learners. In this paper we develop technique to detect the type of a question, based on which the proper technique for extracting the answer is used. The system returns only blocks or phrases of data containing the answer rather than full documents. Therefore, we can highly improve the efficiency of Web QA systems for e-learning.

scholarly journals Social Marketplace Monitoring and Sentiment Analysis

2019 ◽  
pp. 127-133
Author(s):  
P. Monisha ◽  
R. Rubanya ◽  
N. Malarvizhi
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Natural Language Text ◽  
Domain Specific ◽  
Keyboard Input ◽  
On Line ◽  
The Given ◽  
Domain Independent ◽  
Language Text ◽  
Candidate Feature

The overwhelming majority of existing approaches to opinion feature extraction trust mining patterns for one review corpus, ignoring the nontrivial disparities in word spacing characteristics of opinion options across completely different corpora. During this research a unique technique to spot opinion options from on-line reviews by exploiting the distinction in opinion feature statistics across two corpora, one domain-specific corpus (i.e., the given review corpus) and one domain-independent corpus (i.e., the contrasting corpus). The tendency to capture this inequality called domain relevance (DR), characterizes the relevancy of a term to a text assortment. The tendency to extract an inventory of candidate opinion options from the domain review corpus by shaping a group of grammar dependence rules. for every extracted candidate feature, to have a tendency to estimate its intrinsic-domain relevancy (IDR) and extrinsic-domain relevance(EDR) scores on the domain-dependent and domain-independent corpora, severally. Natural language processing (NLP) refers to computer systems that analyze, attempt understand, or produce one or more human languages, such as English, Japanese, Italian, or Russian. Process information contained in natural language text. The input might be text, spoken language, or keyboard input. The field of NLP is primarily concerned with getting computers to perform useful and interesting tasks with human languages. The field of NLP is secondarily concerned with helping us come to a better understanding of human language

A Review on Question Generation from Natural Language Text

2022 ◽  
Vol 40 (1) ◽  
pp. 1-43
Author(s):  
Ruqing Zhang ◽  
Jiafeng Guo ◽  
Lu Chen ◽  
Yixing Fan ◽  
Xueqi Cheng
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Data Augmentation ◽  
Text Structure ◽  
Current Status ◽  
Question Generation ◽  
Natural Language Text ◽  
Question Answering Systems ◽  
The Right ◽  
Language Text

Question generation is an important yet challenging problem in Artificial Intelligence (AI), which aims to generate natural and relevant questions from various input formats, e.g., natural language text, structure database, knowledge base, and image. In this article, we focus on question generation from natural language text, which has received tremendous interest in recent years due to the widespread applications such as data augmentation for question answering systems. During the past decades, many different question generation models have been proposed, from traditional rule-based methods to advanced neural network-based methods. Since there have been a large variety of research works proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we try to provide a more comprehensive taxonomy of question generation tasks from three different perspectives, i.e., the types of the input context text, the target answer, and the generated question. We take a deep look into existing models from different dimensions to analyze their underlying ideas, major design principles, and training strategies We compare these models through benchmark tasks to obtain an empirical understanding of the existing techniques. Moreover, we discuss what is missing in the current literature and what are the promising and desired future directions.

scholarly journals NLP TOKEN MATCHING ON DATABASE USING BINARY SEARCH

2012 ◽  
Vol 3 (1) ◽  
pp. 140-143
Author(s):  
Ekta Aggarwal ◽  
Shreeja Nair
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Time Complexity ◽  
Binary Search ◽  
Translation Process ◽  
Query Translation ◽  
Natural Language Text ◽  
Natural Language Interface ◽  
Reduced Time ◽  
Language Text

Natural Language Processing (NLP) is an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech to do useful things. The paper deals with the concept of database where by the data resources data can be fetched and accessed accordingly with reduced time complexity. The retrieval techniques are pointed out based on the ideas of binary search. A natural language interface refers to words in its own dictionary as well as to the words in the standard dictionary, in order to interpret a query. The main contribution of this investigation is addressing the problem of improving the accuracy of the query translation process by using the information provided by the database schema.  

scholarly journals Event Extraction and Classification from English Articles

2019 ◽  
Vol 8 (2) ◽  
pp. 2861-2865
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Event Extraction ◽  
Natural Language Text ◽  
Digital World ◽  
Accuracy Measure ◽  
Question Answering Systems ◽  
English Articles ◽  
Information Finding ◽  
Language Text

Today’s digital world huge number of information sources like wikis, web, blogs and other sources are creating a lot of information with several events. Basically, an event can be a situation, action or state that can be represented in natural language text in the form of happening or occurrence. Analyzing the event information finding the relation between the events is one of the crucial tasks in information retrieval. In a formal way, the event can be defined as a real-world entity that happens or occur; these are the dynamic occurrences which have causes or effects (E.g. earthquake, floods, crime, etc.). Extracting events, events fall within a timelines extraction can be applied in many of the natural language applications like text summarization, temporal question answering systems, etc. Event extraction and classification can use in other related text searches like News domains, legal documents, wikis, manuscripts, and time-based searches. In this paper, we present a methodology for event extraction in natural language text which helps in finding out the type of an event and classifies the events under specific categories. Our work aims to develop a system which would automatically identify events from articles generated over the internet. The system would not only detect the events but also tried to detect important times of the event. Finally compared the accuracy of work with several classifiers and obtained results shows good accuracy measure for Support Vectors machine (SVM).

scholarly journals Novel rules for extracting the entities of entity relationship models

2021 ◽  
Vol 20 (2) ◽  
pp. 29-35
Author(s):  
Mussa Omar ◽  
Abdulrhman Alsheky ◽  
Balha Faiz
Keyword(s):  
Machine Learning ◽  
Natural Language ◽  
Performance Comparison ◽  
Dramatic Improvement ◽  
Natural Language Text ◽  
Learning Rules ◽  
Linguistic Rules ◽  
Entity Relationship ◽  
Entity Relationships ◽  
Language Text

Extracting entities from natural language text to design conceptual models of the entity relationships is not trivial and novice designers and students can find it especially difficult. Researchers have suggested linguistic rules/guidelines for extracting entities from natural language text. Unfortunately, while these guidelines are often correct they can, also, be invalid. There is no rule that is true at all times. This paper suggests novel rules based on the machine learning classifiers, the RIPPER, the PART and the decision trees. Performance comparison was made between the linguistic and the machine learning rules. The results shows that there was a dramatic improvement when machine learning rules were used.

scholarly journals An Implementation of Natural Language Processing and Text Mining in Stroke Research

2021 ◽  
Vol 39 (3) ◽  
pp. 121-128
Author(s):  
Chulho Kim
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Information ◽  
Clinical Decision ◽  
Health Records ◽  
Natural Language Text ◽  
Stroke Research ◽  
Text Preprocessing ◽  
Language Text

Natural language processing (NLP) is a computerized approach to analyzing text that explores how computers can be used to understand and manipulate natural language text or speech to do useful things. In healthcare field, these NLP techniques are applied in a variety of applications, ranging from evaluating the adequacy of treatment, assessing the presence of the acute illness, and the other clinical decision support. After converting text into computer-readable data through the text preprocessing process, an NLP can extract valuable information using the rule-based algorithm, machine learning, and neural network. We can use NLP to distinguish subtypes of stroke or accurately extract critical clinical information such as severity of stroke and prognosis of patients, etc. If these NLP methods are actively utilized in the future, they will be able to make the most of the electronic health records to enable optimal medical judgment.

scholarly journals Declarative Question Answering over Knowledge Bases Containing Natural Language Text with Answer Set Programming

2019 ◽  
Vol 33 ◽  
pp. 3003-3010 ◽  
Author(s):  
Arindam Mitra ◽  
Peter Clark ◽  
Oyvind Tafjord ◽  
Chitta Baral
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Answer Set Programming ◽  
Knowledge Bases ◽  
Logical Reasoning ◽  
Natural Language Text ◽  
Question Answering Systems ◽  
Logical Representation ◽  
Language Text ◽  
Answer Set

While in recent years machine learning (ML) based approaches have been the popular approach in developing endto-end question answering systems, such systems often struggle when additional knowledge is needed to correctly answer the questions. Proposed alternatives involve translating the question and the natural language text to a logical representation and then use logical reasoning. However, this alternative falters when the size of the text gets bigger. To address this we propose an approach that does logical reasoning over premises written in natural language text. The proposed method uses recent features of Answer Set Programming (ASP) to call external NLP modules (which may be based on ML) which perform simple textual entailment. To test our approach we develop a corpus based on the life cycle questions and showed that Our system achieves up to 18% performance gain when compared to standard MCQ solvers.

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