scholarly journals Legal Question Answering System using Neural Attention

10.29007/4l2q ◽  
2018 ◽  
Author(s):  
Ayaka Morimoto ◽  
Daiki Kubo ◽  
Motoki Sato ◽  
Hiroyuki Shindo ◽  
Yuji Matsumoto
Keyword(s):  
Neural Networks ◽  
Standard Method ◽  
Question Answering ◽  
Phase 1 ◽  
Civil Law ◽  
Weighted Sum ◽  
Phase 2 ◽  
Relevant Article ◽  
Question Answering System ◽  
The Given

This year’s COLIEE has two tasks called phases 1 and 2. The phase 1 needs to find the relevant article given a query t2, and the phase 2 needs to answer whether the given query t2 is yes or no according to Japan civil law articles.This paper presents our proposals for the phase 2 task. Two methods are presented. The first goes along the standard method taken by many authors, such that the relevant article t1 is selected by the similarity to the query t2 at the requirement (condition) and the effect (conclusion) descriptions of the articles. The second is our new proposal, in which Neural Networks with attention mechanism are applied to all the civil law articles in deciding the truthness of the query t2. This method takes into account all the articles by properly calculating their weighted sum.

scholarly journals Hierarchy based firefly optimized K-means clustering for complex question answering

2020 ◽  
Vol 17 (1) ◽  
pp. 264
Author(s):  
A. Chandra Obula Reddy ◽  
K. Madhavi
Keyword(s):  
Question Answering ◽  
Selection Process ◽  
Document Retrieval ◽  
Question Answering System ◽  
Candidate Solution ◽  
Firefly Optimization ◽  
The Given ◽  
Firefly Optimization Algorithm ◽  
Complex Question

Complex Question Answering (CQA) is commonly used for answering community questions which requires human knowledge for answering them. It is essential to find complex question answering system for avoiding the complexities behind the question answering system. In the present work, we proposed Hierarchy based Firefly Optimized k-means Clustering (HFO-KC) method for complex question answering. Initially, the given input query is preprocessed. It eliminates the way of misclassification when comparing the strings. In order to enhance the answer selection process, the obtained keywords are mapped into the candidate solution. After mapping, the obtained keywords are segmented. Each segmentation forms a new query for answer selection and various number of answers selected for each query. Okapi-25 similarity computation is utilized for the process of document retrieval. Then the answers selected are classified with K means clustering which forms the hierarchy for each answer. Finally the firefly optimization algorithm is used for selecting the best quality of answer from the hierarchy.

scholarly journals Automatic question-answer pairs generation and question similarity mechanism in question answering system

2021 ◽  
Author(s):  
Shivani G. Aithal ◽  
Abishek B. Rao ◽  
Sanjay Singh
Keyword(s):  
Common Sense ◽  
Question Answering ◽  
Similarity Score ◽  
Question Answering System ◽  
The Past ◽  
Essential Question ◽  
Question Answering Systems ◽  
Full Benefit ◽  
The Given

AbstractWith the swift growth of the information over the past few years, taking full benefit is increasingly essential. Question Answering System is one of the promising methods to access this much information. The Question Answering System lacks humans’ common sense and reasoning power and cannot identify unanswerable questions and irrelevant questions. These questions are answered by making unreliable and incorrect guesses. In this paper, we address this limitation by proposing a Question Similarity mechanism. Before a question is posed to a Question-Answering system, it is compared with possible generated questions of the given paragraph, and then a Question Similarity Score is generated. The Question Similarity mechanism effectively identifies the unanswerable and irrelevant questions. The proposed Question Similarity mechanism incorporates a human way of reasoning to identify unanswerable and irrelevant questions. This mechanism can avoid the unanswerable and irrelevant questions altogether from being posed to the Question Answering system. It helps the Question Answering Systems to focus only on the answerable questions to improve their performance. Along with this, we introduce an application of the Question Answering System that generates the question-answer pairs given a passage and is useful in several fields.

scholarly journals Prediction of Answer Keywords using Char-RNN

2019 ◽  
Vol 9 (3) ◽  
pp. 2164
Author(s):  
Pratheek I ◽  
Joy Paulose
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Language Processing ◽  
Question Answering ◽  
Context Aware ◽  
Question Answering System ◽  
Novel Approach ◽  
Current Affairs ◽  
Generating Sequences ◽  
Gated Recurrent Unit

<p>Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth.</p>

scholarly journals An Augmented Reality Question Answering System Based on Ensemble Neural Networks

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 17425-17435 ◽  
Author(s):  
Chi-Hua Chen ◽  
Chen-Ling Wu ◽  
Chi-Chun Lo ◽  
Feng-Jang Hwang
Keyword(s):  
Neural Networks ◽  
Augmented Reality ◽  
Question Answering ◽  
Question Answering System ◽  
Ensemble Neural Networks

scholarly journals Recognizing entailments in legal texts using sentence encoding-based and decomposable attention models

10.29007/gn47 ◽  
2018 ◽  
Author(s):  
Nguyen Truong Son ◽  
Phan Viet Anh ◽  
Nguyen Le Minh
Keyword(s):  
Question Answering ◽  
Learning To Rank ◽  
Second Phase ◽  
Learning Models ◽  
Question Answering System ◽  
Attention Model ◽  
Legal Texts ◽  
Cosine Distance ◽  
The Given ◽  
Inference Task

This paper presents an end-to-end question answering system for legal texts. This system includes two main phases. In the first phase, our system will retrieve articles from Japanese Civil Code that are relevant with the given question using the cosine distance after the given question and articles are converted into vectors using TF-IDF weighting scheme. Then, a ranking model can be applied to re-rank these retrieved articles using a learning to rank algorithm and annotated corpus. In the second phase, we adapted two deep learning models, which has been proposed for the Natural language inference task, to check the entailment relationship between a question and its related articles including a sentence encoding-based model and a decomposable attention model. Experimental results show that our approaches can be a promising approach for information extraction/entailment in legal texts.

scholarly journals Visual Question Answering using Convolutional Neural Networks

2021 ◽  
Vol 12 (1S) ◽  
pp. 170-175
Author(s):  
K. P. Moholkar, Et. al.
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Recurrent Neural Network ◽  
Question Answering ◽  
Question Answering System ◽  
Visual Question Answering ◽  
Generalized System ◽  
Multiple Scenarios

The ability of a computer system to be able to understand surroundings and elements and to think like a human being to process the information has always been the major point of focus in the field of Computer Science. One of the ways to achieve this artificial intelligence is Visual Question Answering. Visual Question Answering (VQA) is a trained system which can answer the questions associated to a given image in Natural Language. VQA is a generalized system which can be used in any image-based scenario with adequate training on the relevant data. This is achieved with the help of Neural Networks, particularly Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In this study, we have compared different approaches of VQA, out of which we are exploring CNN based model. With the continued progress in the field of Computer Vision and Question answering system, Visual Question Answering is becoming the essential system which can handle multiple scenarios with their respective data.

Rationalization and internalization

2001 ◽  
Vol 60 (4) ◽  
pp. 215-230 ◽  
Author(s):  
Jean-Léon Beauvois
Keyword(s):  
Phase 1 ◽  
Facilitatory Effect ◽  
Phase 2 ◽  
Causal Explanations ◽  
Reduction Effect ◽  
Dissonance Reduction

After having been told they were free to accept or refuse, pupils aged 6–7 and 10–11 (tested individually) were led to agree to taste a soup that looked disgusting (phase 1: initial counter-motivational obligation). Before tasting the soup, they had to state what they thought about it. A week later, they were asked whether they wanted to try out some new needles that had supposedly been invented to make vaccinations less painful. Agreement or refusal to try was noted, along with the size of the needle chosen in case of agreement (phase 2: act generalization). The main findings included (1) a strong dissonance reduction effect in phase 1, especially for the younger children (rationalization), (2) a generalization effect in phase 2 (foot-in-the-door effect), and (3) a facilitatory effect on generalization of internal causal explanations about the initial agreement. The results are discussed in relation to the distinction between rationalization and internalization.

PENERAPAN MODEL QUANTUM LEARNING UNTUK MENINGKATKAN HASIL BELAJAR AUTOCAD SISWA KELAS X TEKNIK PEMESINAN SMK NEGERI 1 STABAT

2013 ◽  
Vol 5 (1) ◽  
Author(s):  
Abdul Hasan Saragih
Keyword(s):  
Positive Response ◽  
Phase 1 ◽  
First Grade ◽  
Learning Model ◽  
Second Phase ◽  
Phase 2 ◽  
Phase 3 ◽  
The Third ◽  
Third Phase ◽  
Quantum Learning

This classroom research was conducted on the autocad instructions to the first grade of mechinary class of SMK Negeri 1 Stabat aiming at : (1) improving the student’ archievementon autocad instructional to the student of mechinary architecture class of SMK Negeri 1 Stabat, (2) applying Quantum Learning Model to the students of mechinary class of SMK Negeri 1 Stabat, arising the positive response to autocad subject by applying Quantum Learning Model of the students of mechinary class of SMK Negeri 1 Stabat. The result shows that (1) by applying quantum learning model, the students’ achievement improves significantly. The improvement ofthe achievement of the 34 students is very satisfactory; on the first phase, 27 students passed (70.59%), 10 students failed (29.41%). On the second phase 27 students (79.41%) passed and 7 students (20.59%) failed. On the third phase 30 students (88.24%) passed and 4 students (11.76%) failed. The application of quantum learning model in SMK Negeri 1 Stabat proved satisfying. This was visible from the activeness of the students from phase 1 to 3. The activeness average of the students was 74.31% on phase 1,81.35% on phase 2, and 83.63% on phase 3. (3) The application of the quantum learning model on teaching autocad was very positively welcome by the students of mechinary class of SMK Negeri 1 Stabat. On phase 1 the improvement was 81.53% . It improved to 86.15% on phase 3. Therefore, The improvement ofstudent’ response can be categorized good.

Sign in / Sign up

Export Citation Format

Share Document