Common Sense-Based Reasoning Using External Knowledge for Question Answering

Yang, Yunyeong; Kang, Sangwoo

doi:10.1109/access.2020.3045762

Cited by 5 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…as auxiliary knowledge in addition to information from the text. Present-day scholars are also investigating the introduction of external knowledge into machine reading comprehension; for example, Jiang et al [27] in 2020 propose the use of external knowledge in the form of triads and corpora, Yang et al [28] in 2020 propose a model for answering questions by searching for external knowledge, Duan et al [29] in 2021 propose a model that can combine external knowledge and contextual fusion network, and Van et al [30] proposed a model to analyze questions using external knowledge in 2020, all of these models using external knowledge have achieved good performance, which also shows the direction for our future research. In our subsequent work, we will focus on how to effectively incorporate external knowledge into machine reading comprehension and, in response to the importance of sentence semantic vectors for reading comprehension, we will continue to investigate how to convert word vectors of articles into sentence vectors.…”

Section: Discussionmentioning

confidence: 99%

SSIN: Sentence Semantic Interaction Network for Multi-Choice Reading Comprehension

Tohti

Hamdulla

2022

IEEE Access

View full text Add to dashboard Cite

Multiple-choice reading comprehension is a challenging task in natural language processing, which aims to select the correct answer from a set of candidate options when given passage and question. Previous approaches usually focus only on word vector interactions and ignore the importance of sentence semantics for reading when modeling the relationship between passage and question. However, reading is a process that includes complex interactions of various knowledge such as vocabulary, syntax and semantics. Interactions based on word vectors alone do not effectively capture the relationship between passage and question. In this work, we propose the Sentence Semantic Interaction Network (SSIN), which models the relationship among passage, question, and answer options based on sentence semantics. The experimental results show that superior results are achieved on both the RACE and MCTest datasets, confirming that the interaction based on sentence semantic vectors can effectively improve the performance of the model reading comprehension.

show abstract

Section: Discussionmentioning

confidence: 99%

SSIN: Sentence Semantic Interaction Network for Multi-Choice Reading Comprehension

Tohti

Hamdulla

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Jin et al (2020) proposed a classification model, also based on the CNN structure, to distinguish agricultural questions and their short text answers. Yang and Yang (2020) also investigated the question answering task, with the focus on common crop disease. Different from previous studies, we focused on the task of automatically extracting important information from text, organizing and presenting it to farmers to facilitate their capabilities in preventing and dealing with pests and disease in fields.…”

mentioning

confidence: 99%

Relation Extraction for Knowledge Graph Generation in the Agriculture Domain: A Case Study on Soybean Pests and Disease

Wang¹,

Zhang²,

Wang³

et al. 2023

Applied Engineering in Agriculture

View full text Add to dashboard Cite

HighlightsWith the aim to reduce the burden of acquiring expert knowledge and strengthen the connection between written knowledge and the fields, this article investigated the problem of automatically extracting and organizing soybean pests and disease knowledge from text.Entities and relations were extracted using multiple models with deep neural network structures. Performance of these models were compared and evaluated in detail.A knowledge graph was automatically constructed using the extracted information, and made publicly available.ABSTRACT. Precision agriculture is an emerging type of agriculture that intensively uses information technology to automate agricultural production. Soybean (Glycine max (L.) Merri.), is an important crop in China, with an annual demand of approximately 110 million tons. However, in China, soybean production is threatened by more than 30 kinds of disease and 100 kinds of pests. With the rapidly increasing specialized information in the literature, it is difficult for farmers to keep up. Relation extraction automatically identifies and extracts structured knowledge from natural language text and thus can help to alleviate the problem. In this study, we propose to employ relation extraction to systematically extract information from expert-written text, and generate a knowledge graph from the extracted information. This case study was planned in China, therefore we mainly used Chinese texts. Firstly, we carefully chose expert-written text on soybean pests and disease, labeled the entities, and classified their thematic relations into five categories. Then, we built and trained three relation extraction models using state-of-the-art deep learning architectures and evaluated their performance on our task. Finally, we constructed an example knowledge graph from the extracted information and demonstrated their potential usage for automatic reasoning and solution recommendation for pests and disease prevention. In total, this study sampled 1038 entities and 1569 relation instances. Experimental results showed that our best model achieved an F1 score of 98.49% on identifying relations from text. Experimental results also showed the effectiveness of the example knowledge graph. Keywords: Bidirectional encoder representation from transformers, Knowledge graph, Relation extraction, Soybean pests and disease.

show abstract

Automated Multi-Choice Question Answering System using Natural Language Processing

Madoo,

Hole

2024

2024 3rd International Conference for Innovation in Technology (INOCON)

View full text Add to dashboard Cite

Common Sense-Based Reasoning Using External Knowledge for Question Answering

Cited by 5 publications

References 14 publications

SSIN: Sentence Semantic Interaction Network for Multi-Choice Reading Comprehension

SSIN: Sentence Semantic Interaction Network for Multi-Choice Reading Comprehension

Relation Extraction for Knowledge Graph Generation in the Agriculture Domain: A Case Study on Soybean Pests and Disease

Automated Multi-Choice Question Answering System using Natural Language Processing

Contact Info

Product

Resources

About