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A Joint Model of Entity Recognition and Predicate Mapping for Chinese Knowledge Base Question Answering

A Joint Model of Entity Recognition and Predicate Mapping fo...

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international conference on Behavior, Economic and Social Computing (BESC)

作者： Hongjing Li Lin Li Wuhan University Of Technology Wuhan China

ISBN: (数字)9781728186054

ISBN: (纸本)9781728186061

Knowledge base question answering(KBQA) is the key technology of natural language processing. How to understand the semantic information of the natural language problem and capture the semantic relationship between the problem and the structured triples are the problems that KBQA needs to solve. the boundary of subject entities in Chinese questions is not as clear as English, which increases the difficulty of entity recognition. Besides, the variable Chinese grammar makes predicate mapping more difficult for semantic analysis. Existing KBQA is usually implemented using a pipeline model, which has two disadvantages: (1) Errors caused by entity recognition will be propagated to predicate mapping. (2) Neither entity recognition nor predicate mapping can benefit from the information available to each other. So we propose a BERT-based KBQA to joint entity recognition and predicate mapping tasks that use their dependencies to improve model performance. BERT can solve the semantic ambiguity of the Chinese Q&A databases and improve the accuracy of Chinese Knowledge Base Question Answering(CKBQA). the model achieved an F1 score of 92.04% on the NLPCC 2016 KBQA dataset.

关键词： Social computing Computational modeling Semantics Knowledge based systems Pipelines Natural language processing Task analysis

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Extracting Structured Data from Unstructured Text Using Conditional Random Field and Jaccard Similarity 2019

Extracting Structured Data from Unstructured Text Using Cond...

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Proceedings of the 2019 7th international conference on information technology: IoT and Smart City

作者： Sarun Wiriyapistan Sukree Sinthupinyo Department of Computer Engineer Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9781450376631

Nowadays, purchasing information is usually found in form of formal messages such as an email. However, most messages are written in natural language form which is difficult to extract data. Our approach paper uses Conditional Random Fields and Words Similarity to extract data from customers' purchase order emails. We started from dividing the words in the email into a sequence of words. then, we added features of each word. After that, we establish an appropriate template, characteristics and training sample sequences. Next, purchasing information was used for text extraction. From our experiment, it was found that adapting Words Similarity with Conditional Random fields can enhance the accuracy of extracting data significantly.

关键词： Unstructured Text Text Similarity Conditional Random Fields Natural language processing Jaccard Similarity

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Development of Indonesian language Speech Recognition Algorithm Model in Knowledge Database

Development of Indonesian Language Speech Recognition Algori...

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international conference on information technology, Computer and Electrical Engineering (ICITACEE)

作者： Emny Harna Yossy Syaeful Karim Dannys Dian Rachmantyo BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

ISBN: (数字)9781728172262

ISBN: (纸本)9781728172279

Research on humanoid robots in Indonesia is developing very rapidly, marked by research on humanoid robots having the ability to learn independently using Indonesian and vocabulary to receive and provide responses from humans based on Natural language processing. the general objective of research focuses on speech recognition systems to be able to interact with people naturally. Humanoid robots must be able to consider the question style and infer answers through automated, unreliable conversations in noisy environments. In development, there are still shortcomings that is if the answer is not in the knowledge base, the robot will look for the context that is most similar to the knowledge base, instead of giving an answer, the robot gives an answer `not found'. Sometimes, the wrong answer will be displayed because the cosine value between the question and the wrong answer is influenced by the same context, that the truth is not the actual answer. Based on these problems, this study was conducted with the aim of producing structured data and information knowledge according to the topic of the question, producing knowledge storage designs, optimizing models based on design and knowing the performance of knowledge representation storage models. Based on these objectives, this research will design algorithms and modeling representation of Indonesian language knowledge that will be applied to humanoid robots. the expected output is an algorithm design and storage of knowledge representation on humanoid robots.

关键词： Vocabulary Knowledge based systems Humanoid robots Speech recognition Knowledge representation Data models Noise measurement

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Open-Domain Dialogue Generation: Presence, Limitation and Future Directions 2019

Open-Domain Dialogue Generation: Presence, Limitation and Fu...

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Proceedings of the 2019 7th international conference on information technology: IoT and Smart City

作者： Haodong Yang Wenge Rong Zhang Xiong School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781450376631

Dialogue systems have received more and more attention from researchers, especially open-domain dialogue generation system. the application scenario of it, also chat-bot, is very extensive, from website customer service to dialogue robot such as Siri and Microsoft Xiaoice. More and more studies have been done on open-domain dialogue generation. In recent years, deep learning is gradually emerging which can leverage a massive amount of data to learn meaningful feature representations. In this paper, we first give the development of deep learning in Natural language processing(NLP) especially in dialogue generation. Next we analyze the current problems and introduce several improvements which are the current research hotspot. the purpose of this paper is to sort out the research results of recent years and make contributions to the research and development of the dialogue system in the future.

关键词： Natural language processing Deep Learning Dialogue Generation

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A web-based Tool for Semi-interactively Karyotyping the Chromosome Images for Analyzing Chromosome Abnormalities

A Web-based Tool for Semi-interactively Karyotyping the Chro...

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National Foundation for Science and technology Development conference on information and Computer Science (NICS)

作者： Nguyen Hong thinh Nguyen Huu Hoang Son Pham thi Viet Huong Nguyen thi Cuc Nhung Do thi Ram Nguyen thanh Binh Minh Luu Manh Ha FET University of Engineering and Technology VNU Hanoi Vietnam AVITECH University of Engineering and Technology VNU Hanoi Vietnam International School VNU Hanoi Vietnam Hematology and Blood Transfusion Center Bach Mai Hospital Hanoi Vietnam

ISBN: (数字)9780738105536

ISBN: (纸本)9781665405188

Chromosome abnormalities relate to several genetic diseases. these abnormalities can be diagnosed based on the analysis of karyogram of the human chromosomes. However, the manual chromosome karyotyping process is often time consuming. this paper presents a novel web-based tool, Biochrom, to assist the cytogeneticist in producing the karyogram. Biochrom is a semi-automated tool, which provides both manual and automated functions in chromosomes segmentation and classification using image processing combined with machine learning techniques. the study is carried on 612 metaphase images with 48 of those containing abnormal chromosomes. We compare the proposed tool to a conventional public tool, Metasel, for karyotyping based on performance by two cytogeneticists using user experience metrics such as number of manual interactions and processing time. Moreover, we quantitatively evaluate the accuracy of the classification of two approaches: Support Vector Machine (SVM) and deep learning. the evaluation results show that the deep learning classification outperforms SVM classification, and our proposed tool requires fewer interactions and less time consuming to complete the karyotyping task on average.

关键词： Support vector machines Deep learning Manuals Tools User experience Task analysis Biological cells

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Java session language: A session-based language for cloud service composition 7

Java session language: A session-based language for cloud se...

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7th international conference on information technology: IoT and Smart City, ICIT 2019

作者： Jiang, Dongming Jiang, Yuan School of Mathematics and Computer Science Jiangxi Science and Technology Normal University Nanchang330038 China School of Information Engineering Nanchang University Nanchang330047 China

ISBN: (纸本)9781450376631

Since the limited capability of single one, cloud services need to be assembled to form business process, which crate for users demands. However, to realize service composition is a time-consuming task which requires users know both the business logic and techniques of service composition. Its main reason is that current solution lacks a suitable language which can implement cloud service and easy to use. Regarding this issue, we put forward JSL, a new dialect language for integrating services. Because interaction is the basis of service composition, JSL apply session to depict the operation of interaction, then express complex composition further. JSL also use functions operators to shield the prone-to-error concurrent programming. Moreover, we designed model-driven framework to support JSL Simulation experiences demonstrates the efficiency of JSL since JSL and its framework can speed up the development and decrease the concurrent mistakes. © 2019 Association for Computing Machinery.

关键词： web services

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Classification Benchmarks for Under-resourced Bengali language based on Multichannel Convolutional-LSTM Network

Classification Benchmarks for Under-resourced Bengali Langua...

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international conference on Data Science and advanced Analytics (DSAA)

作者： Md. Rezaul Karim Bharathi Raja Chakravarthi John P. McCrae Michael Cochez Fraunhofer Institute for Applied Information Technology FIT Germany Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Ireland Vrije Universiteit Amsterdam the Netherlands

ISBN: (数字)9781728182063

ISBN: (纸本)9781728182070

Exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expressions and individual voices, but also enables people to express anti-social behavior like online harassment, cyberbul-lying, and hate speech. Numerous works have been proposed to utilize these data for social and anti-social behavior analysis, document characterization, and sentiment analysis by predicting the contexts mostly for highly resourced languages like English. However, some languages are under-resources, e.g., South Asian languages like Bengali, Tamil, Assamese, Malayalam, that lack of computational resources for natural language processing. In this paper 1 , we provide several classification benchmarks for Bengali, an under-resourced language. We prepared three datasets of expressing hate, commonly used topics, and opinions for hate speech detection, document classification, and sentiment analysis. We built the largest Bengali word embedding models to date based on 250 million articles, which we call BengFastText. We perform three experiments, covering document classification, sentiment analysis, and hate speech detection. We incorporate word embeddings into a Multichannel Convolutional-LSTM (MC-LSTM) network for predicting different types of hate speech, document classification, and sentiment analysis. Experiments demonstrate that BengFastText can capture the semantics of words from respective contexts correctly. Evaluations against several baseline embedding models, e.g., Word2Vec and GloVe yield up to 92.30%, 82.25%, and 90.45% F1-scores in case of document classification, sentiment analysis, and hate speech detection, respectively during 5-fold cross-validation tests.

关键词： Social networking (online) Sentiment analysis Feature extraction Task analysis Voice activity detection Support vector machines Manuals

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FONDUE: Framework for Node Disambiguation Using Network Embeddings

FONDUE: Framework for Node Disambiguation Using Network Embe...

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international conference on Data Science and advanced Analytics (DSAA)

作者： Ahmad Mel Bo Kang Jefrey Lijffijt Tijl De Bie IDLab Ghent University Ghent Belgium

ISBN: (数字)9781728182063

ISBN: (纸本)9781728182070

Real-world data often presents itself in the form of a network. Examples include social networks, citation networks, biological networks, and knowledge graphs. In their simplest form, networks represent real-life entities (e.g. people, papers, proteins, concepts) as nodes, and describe them in terms of their relations with other entities by means of edges between these nodes. this can be valuable for a range of purposes from the study of information diffusion to bibliographic analysis, bioinformatics research, and question-answering. the quality of networks is often problematic though, affecting downstream tasks. this paper focuses on the common problem where a node in the network in fact corresponds to multiple real-life entities. In particular, we introduce FONDUE, an algorithm based on network embedding for node disambiguation. Given a network, FONDUE identifies nodes that correspond to multiple entities, for subsequent splitting. Extensive experiments on fourteen benchmark datasets demonstrate that FONDUE is substantially and uniformly more accurate for ambiguous node identification compared to the existing state-of-the-art with a lower computational cost, while less optimal for determining the best way to split ambiguous nodes.

关键词： Task analysis Inverse problems Social networking (online) Proteins Natural language processing Data science Data integrity

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Generation of Hindi Word Embeddings and their Utilization in Ranking Documents Using Negative Sampling Architecture, t-SNE Visualization and TF-IDF Based Weighted Average of Vectors 9th

Generation of Hindi Word Embeddings and Their Utilization in...

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9th international conference on Innovations in Bio- Inspired Computing and Applications (IBICA) / 7th World Congress on information and Communication Technologies (WICT)

作者： Prabhudesai, Arya Coll Engn Pune Maharashtra India

ISBN: (纸本)9783030166816;9783030166809

Hindi is the official language of India and has over 500 million speakers worldwide. Being a dominant language with a widespread impact, implies the need for development of technologies that cater to its native speakers. In this paper, a text mining based information retrieval model has been developed to generate Hindi word embeddings and their application ranking documents in order of relevance to an input query. Word embeddings are multidimensional vectors that can be created by utilizing the linguistic context of words in a large corpus. To generate the embeddings, a corpus was created from the Hindi Wikipedia dump, on which the skip-gram approach was applied using a neural network based negative sampling-architecture. the weighted average of each word embedding along with its tf-idf score generated the embeddings for each individual document. the cosine-similarity was then calculated between each document vector and the query vector. Using these similarity scores, the documents were ranked in descending order of relevance to the query. Highly relevant rankings were obtained in response to a query input. the results of the model were visualized using the t-SNE visualization method. the accuracy of this method proves that in the process of conversion of words to numeric vectors, the semantic context of the words was preserved.

关键词： Text mining Natural language processing information retrieval

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A semantic approach for topic-based polarity detection: a case study in the Spanish language 7

A semantic approach for topic-based polarity detection: a ca...

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7th international conference on information technology and Quantitative Management (ITQM) - information technology and Quantitative Management Based on Artificial Intelligence

作者： Ramon Hernandez, Alejandro Garcia Lorenzo, Maria Matilde Simon-Cuevas, Alfredo Arco, Leticia Serrano-Guerrero, Jesus Univ Cent Marta Abreu Las Villas Villa Clara 54830 Cuba Univ Tecnol La Habana Jose Antonio Echeverria Havana 11500 Cuba Vrije Univ Brussel B-1050 Brussels Belgium Univ Castilla La Mancha E-13071 Ciudad Real Spain

In recent years, surprising amounts of news, messages, and reviews of products and services are generated in the online social media. Several efforts are being dedicated to detecting topics, as well as mining opinions in these unstructured texts. there are several approaches that compute opinion polarity, and some of them consider topics for their textual analysis. Nevertheless, discovering topics in opinions continues being challenging;due to opinions are generally short and informally written. Besides, opinions do not have a defined structure in several paragraphs;they are presented most of the time as a composition in a paragraph. In this paper, we propose a method to detect polarity in opinions by topics. Our proposal contributes to the fuzzy polarity calculation of detected topics in Spanish opinions. this method is comprised of three main steps: (1) preprocessing, (2) topic detection and (3) fuzzy polarity detection. It is important to notice the added values of this paper are: (1) the topic detection proposal based on the semantic processing when applying the clustering algorithm on opinion sentences, and (2) the evaluation of different aggregation operators for determining the opinion polarity from a fuzzy logic perspective. Aiming at assessing the quality of the resultant polarity detection by topics, we have conducted two main experiments over the Spanish corpus of opinions about Andalusian Hotels from the TripAdvisor site. the results have shown that our method is able to detect the topics correctly, as well as calculating their opinion polarities. (C) 2020 the Authors. Published by Elsevier B.V.

关键词： opinion mining topic detection semantic analysis fuzzy aggregation operators

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