scholarly journals Research on Automatic Question Answering of Generative Knowledge Graph Based on Pointer Network

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 136
Author(s):  
Shuang Liu ◽  
Nannan Tan ◽  
Yaqian Ge ◽  
Niko Lukač
Keyword(s):  
Knowledge Base ◽  
Word Frequency ◽  
Language Processing ◽  
Question Answering ◽  
Language Model ◽  
Word List ◽  
Superior Performance ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Question Answering Systems

Question-answering systems based on knowledge graphs are extremely challenging tasks in the field of natural language processing. Most of the existing Chinese Knowledge Base Question Answering(KBQA) can only return the knowledge stored in the knowledge base by extractive methods. Nevertheless, this processing does not conform to the reading habits and cannot solve the Out-of-vocabulary(OOV) problem. In this paper, a new generative question answering method based on knowledge graph is proposed, including three parts of knowledge vocabulary construction, data pre-processing, and answer generation. In the word list construction, BiLSTM-CRF is used to identify the entity in the source text, finding the triples contained in the entity, counting the word frequency, and constructing it. In the part of data pre-processing, a pre-trained language model BERT combining word frequency semantic features is adopted to obtain word vectors. In the answer generation part, one combination of a vocabulary constructed by the knowledge graph and a pointer generator network(PGN) is proposed to point to the corresponding entity for generating answer. The experimental results show that the proposed method can achieve superior performance on WebQA datasets than other methods.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

scholarly journals Self-Supervised Contextual Data Augmentation for Natural Language Processing

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1393
Author(s):  
Dongju Park ◽  
Chang Wook Ahn
Keyword(s):  
Supervised Learning ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Question Answering ◽  
Data Augmentation ◽  
Language Model ◽  
Contextual Data ◽  
External Data ◽  
Label Information ◽  
Benchmark Datasets

In this paper, we propose a novel data augmentation method with respect to the target context of the data via self-supervised learning. Instead of looking for the exact synonyms of masked words, the proposed method finds words that can replace the original words considering the context. For self-supervised learning, we can employ the masked language model (MLM), which masks a specific word within a sentence and obtains the original word. The MLM learns the context of a sentence through asymmetrical inputs and outputs. However, without using the existing MLM, we propose a label-masked language model (LMLM) that can include label information for the mask tokens used in the MLM to effectively use the MLM in data with label information. The augmentation method performs self-supervised learning using LMLM and then implements data augmentation through the trained model. We demonstrate that our proposed method improves the classification accuracy of recurrent neural networks and convolutional neural network-based classifiers through several experiments for text classification benchmark datasets, including the Stanford Sentiment Treebank-5 (SST5), the Stanford Sentiment Treebank-2 (SST2), the subjectivity (Subj), the Multi-Perspective Question Answering (MPQA), the Movie Reviews (MR), and the Text Retrieval Conference (TREC) datasets. In addition, since the proposed method does not use external data, it can eliminate the time spent collecting external data, or pre-training using external data.

Document Summarization Using Sentence-Level Semantic Based on Word Embeddings

2019 ◽  
Vol 29 (02) ◽  
pp. 177-196 ◽  
Author(s):  
Kamal Al-Sabahi ◽  
Zhang Zuping
Keyword(s):  
Language Processing ◽  
Web Search ◽  
Question Answering ◽  
Information Overload ◽  
Good Representation ◽  
Intelligence Analysis ◽  
Word Embeddings ◽  
Question Answering Systems ◽  
Active Research ◽  
News Recommendation

In the era of information overload, text summarization has become a focus of attention in a number of diverse fields such as, question answering systems, intelligence analysis, news recommendation systems, search results in web search engines, and so on. A good document representation is the key point in any successful summarizer. Learning this representation becomes a very active research in natural language processing field (NLP). Traditional approaches mostly fail to deliver a good representation. Word embedding has proved an excellent performance in learning the representation. In this paper, a modified BM25 with Word Embeddings are used to build the sentence vectors from word vectors. The entire document is represented as a set of sentence vectors. Then, the similarity between every pair of sentence vectors is computed. After that, TextRank, a graph-based model, is used to rank the sentences. The summary is generated by picking the top-ranked sentences according to the compression rate. Two well-known datasets, DUC2002 and DUC2004, are used to evaluate the models. The experimental results show that the proposed models perform comprehensively better compared to the state-of-the-art methods.

Question Answering

2012 ◽  
Author(s):  
Sanda Harabagiu ◽  
Dan Moldovan
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Document Retrieval ◽  
Semantic Features ◽  
Knowledge Based ◽  
Answer Extraction ◽  
Small Set ◽  
Processing Module ◽  
On Line ◽  
Processing Techniques

Textual Question Answering (QA) identifies the answer to a question in large collections of on-line documents. By providing a small set of exact answers to questions, QA takes a step closer to information retrieval rather than document retrieval. A QA system comprises three modules: a question-processing module, a document-processing module, and an answer extraction and formulation module. Questions may be asked about any topic, in contrast with Information Extraction (IE), which identifies textual information relevant only to a predefined set of events and entities. The natural language processing (NLP) techniques used in open-domain QA systems may range from simple lexical and semantic disambiguation of question stems to complex processing that combines syntactic and semantic features of the questions with pragmatic information derived from the context of candidate answers. This article reviews current research in integrating knowledge-based NLP methods with shallow processing techniques for QA.

scholarly journals An Empirical Study of Content Understanding in Conversational Question Answering

2020 ◽  
Vol 34 (05) ◽  
pp. 7578-7585
Author(s):  
Ting-Rui Chiang ◽  
Hao-Tong Ye ◽  
Yun-Nung Chen
Keyword(s):  
Natural Language Processing ◽  
Empirical Study ◽  
Language Processing ◽  
Question Answering ◽  
Source Code ◽  
Content Understanding ◽  
Question Answering Systems ◽  
Benchmark Datasets ◽  
Context Free ◽  
Answering Questions

With a lot of work about context-free question answering systems, there is an emerging trend of conversational question answering models in the natural language processing field. Thanks to the recently collected datasets, including QuAC and CoQA, there has been more work on conversational question answering, and recent work has achieved competitive performance on both datasets. However, to best of our knowledge, two important questions for conversational comprehension research have not been well studied: 1) How well can the benchmark dataset reflect models' content understanding? 2) Do the models well utilize the conversation content when answering questions? To investigate these questions, we design different training settings, testing settings, as well as an attack to verify the models' capability of content understanding on QuAC and CoQA. The experimental results indicate some potential hazards in the benchmark datasets, QuAC and CoQA, for conversational comprehension research. Our analysis also sheds light on both what models may learn and how datasets may bias the models. With deep investigation of the task, it is believed that this work can benefit the future progress of conversation comprehension. The source code is available at https://github.com/MiuLab/CQA-Study.

scholarly journals Super Agent Chatbot “3S” Sebagai Media Informasi Menggunakan Metoda Natural Language Processing(NLP)

2019 ◽  
Vol 2 (1) ◽  
pp. 53-64
Author(s):  
Herwin H Herwin
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Site ◽  
Question Answering ◽  
Question Answering Systems ◽  
Portal Website

STMIK Amik Riau memiliki portal pada website http://www.sar.ac.id difungsikan sebagai media penyebaran informasi bagi sivitas akademika dan stakeholder. Rerata pengunjung setiap hari dalam 3 bulan terakhir adalah 150 kunjungan, namun terjadi peningkatan pada saat penerimaan mahasiswa di setiap tahun akademik. Hal ini mengindikasikan terjadinya peningkatan minat masyarakat untuk mengetahui informasi STMIK Amik Riau. Sayangnya, sampai saat ini pemanfaatan portal web site masih satu arah, dari STMIK Amik Riau ke stakeholder dan masyarakat, tidak terjadi sebaliknya. Komunikasi stakeholder dengan PT sehubungan dengan muatan yang ada di dalam portal menggunakan media sosial dan tidak terintegrasi dengan web.  Begitu juga dengan masukan, koreksi, tanggapan, maupun komunikasi lain menggunakan media sosial.  Sampai saat ini, masyarakat yang mengunjungi portal website baik masyarakat luas, maupun stakeholder tidak dapat dideteksi waktu berkunjung sehingga tidak dapat disapa dengan filosofi “3S”, padahal masyarakat luas yang telah berkunjung merupakan pasar potensial untuk di edukasi. Masyarakat yang berkunjung ke portal website, dengan sopan di sapa oleh sistem, kemudian dilanjutkan dengan komunikasi langsung, tersedia mesin yang siap memberikan salam  dan melayani setiap pertanyaan yang diajukan oleh pengunjung. Penelitian ini bertujuan membuat chatbot yang mampu berkomunikasi dengan pengunjung website.  Chatbot  yang telah dibuat diberi nama STMIK Amik Riau Intelligence Virtual Information disingkat SILVI.  Chatbot dibuat berdasarkan Question Answering Systems (QAS), bekerja dengan algoritma kemiripan antara dua teks. Penelitian ini menghasilkan aplikasi yang siap digunakan, diberi nama SILVI, mampu berkomunikasi dengan pengunjung website. Chatbot mengoptimalkan komunikasi seolah tidak menyadari, tetap menganggap lawan bicara adalah pegawai yang tepat dalam tugas pokok dan fungsi.  

scholarly journals Legal Knowledge Extraction for Knowledge Graph Based Question-Answering

2020 ◽  
Author(s):  
Francesco Sovrano ◽  
Monica Palmirani ◽  
Fabio Vitali
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Design Patterns ◽  
Question Answering ◽  
Knowledge Extraction ◽  
Knowledge Graph ◽  
Legal Knowledge ◽  
Legal Information ◽  
Event Time ◽  
Legal Domain

This paper presents the Open Knowledge Extraction (OKE) tools combined with natural language analysis of the sentence in order to enrich the semantic of the legal knowledge extracted from legal text. In particular the use case is on international private law with specific regard to the Rome I Regulation EC 593/2008, Rome II Regulation EC 864/2007, and Brussels I bis Regulation EU 1215/2012. A Knowledge Graph (KG) is built using OKE and Natural Language Processing (NLP) methods jointly with the main ontology design patterns defined for the legal domain (e.g., event, time, role, agent, right, obligations, jurisdiction). Using critical questions, underlined by legal experts in the domain, we have built a question answering tool capable to support the information retrieval and to answer to these queries. The system should help the legal expert to retrieve the relevant legal information connected with topics, concepts, entities, normative references in order to integrate his/her searching activities.

scholarly journals BUILDING QUESTION ANSWERING SYSTEM BASED ON COMPUTING DOMAIN ONTOLOGY

2020 ◽  
Vol 38 (02) ◽  
Author(s):  
TẠ DUY CÔNG CHIẾN
Keyword(s):  
Language Processing ◽  
Digital Libraries ◽  
Question Answering ◽  
Domain Ontology ◽  
Text Documents ◽  
Question Answering System ◽  
Domain Specific ◽  
Sql Database ◽  
Question Answering Systems ◽  
Education Business

Question answering systems are applied to many different fields in recent years, such as education, business, and surveys. The purpose of these systems is to answer automatically the questions or queries of users about some problems. This paper introduces a question answering system is built based on a domain specific ontology. This ontology, which contains the data and the vocabularies related to the computing domain are built from text documents of the ACM Digital Libraries. Consequently, the system only answers the problems pertaining to the information technology domains such as database, network, machine learning, etc. We use the methodologies of Natural Language Processing and domain ontology to build this system. In order to increase performance, I use a graph database to store the computing ontology and apply no-SQL database for querying data of computing ontology.

scholarly journals Building Graph for Events and Time in Natural Language Text

2020 ◽  
Vol 9 (3) ◽  
pp. 581-586
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Question Answering ◽  
Relation Extraction ◽  
Event Extraction ◽  
Event Time ◽  
Time Graph ◽  
Question Answering Systems

Events and time are two major key terms in natural language processing due to the various event-oriented tasks these are become an essential terms in information extraction. In natural language processing and information extraction or retrieval event and time leads to several applications like text summaries, documents summaries, and question answering systems. In this paper, we present events-time graph as a new way of construction for event-time based information from text. In this event-time graph nodes are events, whereas edges represent the temporal and co-reference relations between events. In many of the previous researches of natural language processing mainly individually focused on extraction tasks and in domain-specific way but in this work we present extraction and representation of the relationship between events- time by representing with event time graph construction. Our overall system construction is in three-step process that performs event extraction, time extraction, and representing relation extraction. Each step is at a performance level comparable with the state of the art. We present Event extraction on MUC data corpus annotated with events mentions on which we train and evaluate our model. Next, we present time extraction the model of times tested for several news articles from Wikipedia corpus. Next is to represent event time relation by representation by next constructing event time graphs. Finally, we evaluate the overall quality of event graphs with the evaluation metrics and conclude the observations of the entire work

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