Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (12)
  • Open Access


    SciCN: A Scientific Dataset for Chinese Named Entity Recognition

    Jing Yang, Bin Ji, Shasha Li*, Jun Ma, Jie Yu

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4303-4315, 2024, DOI:10.32604/cmc.2023.035594

    Abstract Named entity recognition (NER) is a fundamental task of information extraction (IE), and it has attracted considerable research attention in recent years. The abundant annotated English NER datasets have significantly promoted the NER research in the English field. By contrast, much fewer efforts are made to the Chinese NER research, especially in the scientific domain, due to the scarcity of Chinese NER datasets. To alleviate this problem, we present a Chinese scientific NER dataset–SciCN, which contains entity annotations of titles and abstracts derived from 3,500 scientific papers. We manually annotate a total of 62,059 entities, and these entities are classified… More >

  • Open Access


    A Joint Entity Relation Extraction Model Based on Relation Semantic Template Automatically Constructed

    Wei Liu, Meijuan Yin*, Jialong Zhang, Lunchong Cui

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 975-997, 2024, DOI:10.32604/cmc.2023.046475

    Abstract The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities, and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation. However, this method has some problems, such as relying on expert experience and poor portability. Inspired by the rule-based entity relation extraction method, this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed, which is abbreviated as… More >

  • Open Access


    Information Extraction Based on Multi-turn Question Answering for Analyzing Korean Research Trends

    Seongung Jo1, Heung-Seon Oh1,*, Sanghun Im1, Gibaeg Kim1, Seonho Kim2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2967-2980, 2023, DOI:10.32604/cmc.2023.031983

    Abstract Analyzing Research and Development (R&D) trends is important because it can influence future decisions regarding R&D direction. In typical trend analysis, topic or technology taxonomies are employed to compute the popularities of the topics or codes over time. Although it is simple and effective, the taxonomies are difficult to manage because new technologies are introduced rapidly. Therefore, recent studies exploit deep learning to extract pre-defined targets such as problems and solutions. Based on the recent advances in question answering (QA) using deep learning, we adopt a multi-turn QA model to extract problems and solutions from Korean R&D reports. With the… More >

  • Open Access


    Combing Type-Aware Attention and Graph Convolutional Networks for Event Detection

    Kun Ding1, Lu Xu2, Ming Liu1, Xiaoxiong Zhang1, Liu Liu1, Daojian Zeng2,*, Yuting Liu1,3, Chen Jin4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 641-654, 2023, DOI:10.32604/cmc.2023.031052

    Abstract Event detection (ED) is aimed at detecting event occurrences and categorizing them. This task has been previously solved via recognition and classification of event triggers (ETs), which are defined as the phrase or word most clearly expressing event occurrence. Thus, current approaches require both annotated triggers as well as event types in training data. Nevertheless, triggers are non-essential in ED, and it is time-wasting for annotators to identify the “most clearly” word from a sentence, particularly in longer sentences. To decrease manual effort, we evaluate event detection without triggers. We propose a novel framework that combines Type-aware Attention and Graph… More >

  • Open Access


    Contextual Text Mining Framework for Unstructured Textual Judicial Corpora through Ontologies

    Zubair Nabi1, Ramzan Talib1,*, Muhammad Kashif Hanif1, Muhammad Awais2

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1357-1374, 2022, DOI:10.32604/csse.2022.025712

    Abstract Digitalization has changed the way of information processing, and new techniques of legal data processing are evolving. Text mining helps to analyze and search different court cases available in the form of digital text documents to extract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reduced time. The rapid development of judicial ontologies seems to deliver interesting problem solving to legal knowledge formalization. Mining context information through ontologies from corpora is a challenging and interesting field. This research paper presents a three tier contextual text… More >

  • Open Access


    Construction and Application of Knowledge Graph for Quality and Safety Supervision of Transportation Engineering

    Sheng Huang, Chuanle Liu*

    Journal on Artificial Intelligence, Vol.3, No.4, pp. 153-162, 2021, DOI:10.32604/jai.2021.025175

    Abstract Knowledge graph technology play a more and more important role in various fields of industry and academia. This paper firstly introduces the general framework of the knowledge graph construction, which includes three stages: information extraction, knowledge fusion and knowledge processing. In order to improve the efficiency of quality and safety supervision of transportation engineering construction, this paper constructs a knowledge graph by acquiring multi-sources heterogeneous data from supervision of transportation engineering quality and safety. It employs a bottom-up construction strategy and some natural language processing methods to solve the problems of the knowledge extraction for transportation engineering construction. We use… More >

  • Open Access


    Semantic Information Extraction from Multi-Corpora Using Deep Learning

    Sunil Kumar1, Hanumat G. Sastry1, Venkatadri Marriboyina2, Hammam Alshazly3,*, Sahar Ahmed Idris4, Madhushi Verma5, Manjit Kaur5

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5021-5038, 2022, DOI:10.32604/cmc.2022.021149

    Abstract Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learning-based techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity… More >

  • Open Access


    Enhancement of Sentiment Analysis Using Clause and Discourse Connectives

    Kumari Sheeja Saraswathy, Sobha Lalitha Devi*

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1983-1999, 2021, DOI:10.32604/cmc.2021.015661

    Abstract The sentiment of a text depends on the clausal structure of the sentence and the connectives’ discourse arguments. In this work, the clause boundary, discourse argument, and syntactic and semantic information of the sentence are used to assign the text’s sentiment. The clause boundaries identify the span of the text, and the discourse connectives identify the arguments. Since the lexicon-based analysis of traditional sentiment analysis gives the wrong sentiment of the sentence, a deeper-level semantic analysis is required for the correct analysis of sentiments. Hence, in this study, explicit connectives in Malayalam are considered to identify the discourse arguments. A… More >

  • Open Access


    Time-Aware PolarisX: Auto-Growing Knowledge Graph

    Yeon-Sun Ahn, Ok-Ran Jeong*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2695-2708, 2021, DOI:10.32604/cmc.2021.015636

    Abstract A knowledge graph is a structured graph in which data obtained from multiple sources are standardized to acquire and integrate human knowledge. Research is being actively conducted to cover a wide variety of knowledge, as it can be applied to applications that help humans. However, existing researches are constructing knowledge graphs without the time information that knowledge implies. Knowledge stored without time information becomes outdated over time, and in the future, the possibility of knowledge being false or meaningful changes is excluded. As a result, they can’t reflect information that changes dynamically, and they can’t accept information that has newly… More >

  • Open Access


    A Combinatorial Optimized Knapsack Linear Space for Information Retrieval

    Varghese S. Chooralil1, Vinodh P. Vijayan2, Biju Paul1, M. M. Anishin Raj3, B. Karthikeyan4,*, G. Manikandan4

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2891-2903, 2021, DOI:10.32604/cmc.2021.012796

    Abstract Key information extraction can reduce the dimensional effects while evaluating the correct preferences of users during semantic data analysis. Currently, the classifiers are used to maximize the performance of web-page recommendation in terms of precision and satisfaction. The recent method disambiguates contextual sentiment using conceptual prediction with robustness, however the conceptual prediction method is not able to yield the optimal solution. Context-dependent terms are primarily evaluated by constructing linear space of context features, presuming that if the terms come together in certain consumer-related reviews, they are semantically reliant. Moreover, the more frequently they coexist, the greater the semantic dependency is.… More >

Displaying 1-10 on page 1 of 12. Per Page