Journal of Computer Science and Technology ›› 2023, Vol. 38 ›› Issue (1): 196-210.doi: 10.1007/s11390-023-2835-4

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Networks and Distributed Computing

• Special Issue in Honor of Professor Kai Hwang’s 80th Birthday • Previous Articles     Next Articles

Improving Entity Linking in Chinese Domain by Sense Embedding Based on Graph Clustering

Zhao-Bo Zhang (张照博), Member, CCF, Zhi-Man Zhong (钟芷漫), Member, CCF, Ping-Peng Yuan (袁平鹏), Senior Member, CCF, Member, ACM, IEEE, and Hai Jin* (金 海), Fellow, CCF, IEEE, Life Member, ACM        

  1. National Engineering Research Center for Big Data Technology and System, Huazhong University of Science and Technology
    Wuhan 430074, China

    Service Computing Technology and System Laboratory, Huazhong University of Science and Technology
    Wuhan 430074, China

    Cluster and Grid Computing Laboratory, Huazhong University of Science and Technology, Wuhan 430074, China
    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
  • Received:2022-09-16 Revised:2022-10-30 Accepted:2023-01-10 Online:2023-02-28 Published:2023-02-28
  • Contact: Hai Jin E-mail:hjin@hust.edu.cn
  • About author:Hai Jin is a chair professor of computer science and engineering at Huazhong University of Science and Technology, Wuhan. Jin received his Ph.D. degree in computer engineering from Huazhong University of Science and Technology, Wuhan, in 1994. In 1996, he was awarded a German Academic Exchange Service Fellowship to visit the Technical University of Chemnitz, Straβe der Nationen. Jin worked at The University of Hong Kong, Hong Kong, between 1998 and 2000, and as a visiting scholar at the University of Southern California, Los Angeles, between 1999 and 2000. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001. Jin is a CCF Fellow, IEEE Fellow, and a life member of ACM. He has co-authored 22 books and published over 900 research papers. His research interests include computer architecture, virtualization technology, distributed computing, big data processing, network storage, and network security.
  • Supported by:
    The work was supported by the National Natural Science Foundation of China under Grant Nos. 61932004 and 62072205.

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity ranking. It is of great significance to some NLP (natural language processing) tasks, such as question answering. Unlike English entity linking, Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words, which is more evident in certain scenarios. In Chinese domains, such as industry, the generated candidate entities are usually composed of long strings and are heavily nested. In addition, the meanings of the words that make up industrial entities are sometimes ambiguous. Their semantic space is a subspace of the general word embedding space, and thus each entity word needs to get its exact meanings. Therefore, we propose two schemes to achieve better Chinese entity linking. First, we implement an n-gram based candidate entity generation method to increase the recall rate and reduce the nesting noise. Then, we enhance the corresponding candidate entity ranking mechanism by introducing sense embedding. Considering the contradiction between the ambiguity of word vectors and the single sense of the industrial domain, we design a sense embedding model based on graph clustering, which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with context. We test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general scenarios. We confirm that our method can better learn candidate entities’ fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.

Key words: natural language processing (NLP); domain entity linking; computational linguistics; word sense disambiguation; knowledge graph;

<table class="reference-tab" style="background-color:#FFFFFF;width:914.104px;color:#333333;font-family:Calibri, Arial, 微软雅黑, "font-size:16px;"> <tbody> <tr class="document-box" id="b1"> <td valign="top" class="td1"> [1] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Sun C C, Shen D R. Mixed hierarchical networks for deep entity matching. <i>Journal of Computer Science and Technology</i>, 2021, 36(4): 822–838. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1007/s11390-021-1321-0" target="_blank">10.1007/s11390-021-1321-0</a>. </div> </td> </tr> <tr class="document-box" id="b2"> <td valign="top" class="td1"> [2] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Li B Z, Min S, Iyer S, Mehdad Y, Yin W T. Efficient one-pass end-to-end entity linking for questions. In <i>Proc</i>. <i>the 2020 Conference on Empirical Methods in Natural Language Processing</i>, Nov. 2020, pp.6433–6441. DOI: <a href="http://dx.doi.org/10.18653/v1/2020.emnlp-main.522">10.18653/v1/2020.emnlp-main.522</a>. </div> </td> </tr> <tr class="document-box" id="b3"> <td valign="top" class="td1"> [3] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Chen K, Shen G H, Huang Z Q, Wang H J. Improved entity linking for simple question answering over knowledge graph. <i>International Journal of Software Engineering and Knowledge Engineering</i>, 2021, 31(1): 55–80. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1142/S0218194021400039" target="_blank">10.1142/S0218194021400039</a>. </div> </td> </tr> <tr class="document-box" id="b4"> <td valign="top" class="td1"> [4] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Amplayo R K, Lim S, Hwang S W. Entity commonsense representation for neural abstractive summarization. In <i>Proc</i>. <i>the 2018 Conference of the North American Chapter of the Association for Computational Linguistics</i>: <i>Human Language Technologies</i>, Jun. 2018, pp.697–707. DOI: <a href="http://dx.doi.org/10.18653/v1/N18-1064">10.18653/v1/N18-1064</a>. </div> </td> </tr> <tr class="document-box" id="b5"> <td valign="top" class="td1"> [5] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Shen W, Wang J Y, Han J W. Entity linking with a knowledge base: Issues, techniques, and solutions. <i>IEEE Trans. Knowledge and Data Engineering</i>, 2015, 27(2): 443–460. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1109/TKDE.2014.2327028" target="_blank">10.1109/TKDE.2014.2327028</a>. </div> </td> </tr> <tr class="document-box" id="b6"> <td valign="top" class="td1"> [6] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Li M Y, Xing Y Q, Kong F, Zhou G D. Towards better entity linking. <i>Frontiers of Computer Science</i>, 2022, 16(2): 162308. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1007/s11704-020-0192-9" target="_blank">10.1007/s11704-020-0192-9</a>. </div> </td> </tr> <tr class="document-box" id="b7"> <td valign="top" class="td1"> [7] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Fu J L, Qiu J, Guo Y L, Li L. Entity linking and name disambiguation using SVM in Chinese micro-blogs. In <i>Proc</i>. <i>the 11th International Conference on Natural Computation</i>, Aug. 2015, pp.468–472. DOI: <a href="http://dx.doi.org/10.1109/ICNC.2015.7378034">10.1109/ICNC.2015.7378034</a>. </div> </td> </tr> <tr class="document-box" id="b8"> <td valign="top" class="td1"> [8] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Huang D C, Wang J L. An approach on Chinese microblog entity linking combining Baidu encyclopaedia and word2vec. <i>Procedia Computer Science</i>, 2017, 111: 37–45. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1016/j.procs.2017.06.007" target="_blank">10.1016/j.procs.2017.06.007</a>. </div> </td> </tr> <tr class="document-box" id="b9"> <td valign="top" class="td1"> [9] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Zeng W X, Tang J Y, Zhao X. Entity linking on Chinese microblogs via deep neural network. <i>IEEE Access</i>, 2018, 6: 25908–25920. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1109/ACCESS.2018.2833153" target="_blank">10.1109/ACCESS.2018.2833153</a>. </div> </td> </tr> <tr class="document-box" id="b10"> <td valign="top" class="td1"> [10] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Ma C F, Sha Y, Tan J L, Guo L, Peng H L. Chinese social media entity linking based on effective context with topic semantics. In <i>Proc</i>. <i>the 43rd Annual Computer Software and Applications Conference</i>, Jul. 2019, pp.386–395. DOI: <a href="http://dx.doi.org/10.1109/COMPSAC.2019.00063">10.1109/COMPSAC.2019.00063</a>. </div> </td> </tr> <tr class="document-box" id="b11"> <td valign="top" class="td1"> [11] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Chen T Q, Guestrin C. XGBoost: A scalable tree boosting system. In <i>Proc</i>. <i>the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining</i>, Aug. 2016, pp.785–794. DOI: <a href="http://dx.doi.org/10.1145/2939672.2939785">10.1145/2939672.2939785</a>. </div> </td> </tr> <tr class="document-box" id="b12"> <td valign="top" class="td1"> [12] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Moro A, Raganato A, Navigli R. Entity linking meets word sense disambiguation: A unified approach. <i>Trans. Association for Computational Linguistics</i>, 2014, 2: 231–244. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1162/tacl_a_00179" target="_blank">10.1162/tacl_a_00179</a>. </div> </td> </tr> <tr class="document-box" id="b13"> <td valign="top" class="td1"> [13] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Khosrovian K, Pfahl D, Garousi V. GENSIM 2.0: A customizable process simulation model for software process evaluation. In <i>Proc</i>. <i>the 2008 International Conference on Software Process</i>, May 2008, pp.294–306. DOI: <a href="http://dx.doi.org/10.1007/978-3-540-79588-9_26">10.1007/978-3-540-79588-9_26</a>. </div> </td> </tr> <tr class="document-box" id="b14"> <td valign="top" class="td1"> [14] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Hochreiter S, Schmidhuber J. Long short-term memory. <i>Neural Computation</i>, 1997, 9(8): 1735–1780. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1162/neco.1997.9.8.1735" target="_blank">10.1162/neco.1997.9.8.1735</a>. </div> </td> </tr> <tr class="document-box" id="b15"> <td valign="top" class="td1"> [15] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Phan M C, Sun A X, Tay Y, Han J L, Li C L. NeuPL: Attention-based semantic matching and pair-linking for entity disambiguation. In <i>Proc</i>. <i>the 2017 ACM Conference on Information and Knowledge Management</i>, Nov. 2017, pp.1667–1676. DOI: <a href="http://dx.doi.org/10.1145/3132847.3132963">10.1145/3132847.3132963</a>. </div> </td> </tr> <tr class="document-box" id="b16"> <td valign="top" class="td1"> [16] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Zeng W X, Zhao X, Tang J Y, Tan Z, Huang X Q. CLEEK: A Chinese long-text corpus for entity linking. In <i>Proc</i>. <i>the 12th Language Resources and Evaluation Conference</i>, May 2020, pp.2026–2035. DOI: <a href="http://dx.doi.org/10.1145/3132847.3132963">10.1145/3132847.3132963</a>. </div> </td> </tr> <tr class="document-box" id="b17"> <td valign="top" class="td1"> [17] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Lei K, Zhang B, Liu Y, Deng Y, Zhang D Y, Shen Y. A knowledge graph based solution for entity discovery and linking in open-domain questions. In <i>Proc</i>. <i>the 2nd International Conference on Smart Computing and Communication</i>, Dec. 2017, pp.181–190. DOI: <a href="http://dx.doi.org/10.1007/978-3-319-73830-7_19">10.1007/978-3-319-73830-7_19</a>. </div> </td> </tr> <tr class="document-box" id="b18"> <td valign="top" class="td1"> [18] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Inan E, Dikenelli O. A sequence learning method for domain-specific entity linking. In <i>Proc</i>. <i>the 7th Named Entities Workshop</i>, Jul. 2018, pp.14–21. DOI: <a href="http://dx.doi.org/10.18653/v1/W18-2403">10.18653/v1/W18-2403</a>. </div> </td> </tr> <tr class="document-box" id="b19"> <td valign="top" class="td1"> [19] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Logeswaran L, Chang M W, Lee K, Toutanova K, Devlin J, Lee H. Zero-shot entity linking by reading entity descriptions. In <i>Proc</i>. <i>the 57th Annual Meeting of the Association for Computational Linguistics</i>, Jul. 2019, pp.3449–3460. DOI: <a href="http://dx.doi.org/10.18653/v1/P19-1335">10.18653/v1/P19-1335</a>. </div> </td> </tr> <tr class="document-box" id="b20"> <td valign="top" class="td1"> [20] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Chen L H, Varoquaux G, Suchanek F M. A lightweight neural model for biomedical entity linking. <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, 2021, 35(14): 12657–12665. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1609/aaai.v35i14.17499" target="_blank">10.1609/aaai.v35i14.17499</a>. </div> </td> </tr> <tr class="document-box" id="b21"> <td valign="top" class="td1"> [21] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Dong Z D, Dong Q, Hao C L. HowNet and its computation of meaning. In <i>Proc</i>. <i>the 23rd International Conference on Computational Linguistics</i>: <i>Demonstrations</i>, Aug. 2010, pp.53–56. DOI: <a href="http://dx.doi.org/10.5555/1944284.1944298" target="_blank">10.5555/1944284.1944298</a>. </div> </td> </tr> <tr class="document-box" id="b22"> <td valign="top" class="td1"> [22] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Miller G A. WordNet: A lexical database for English. <i>Communications of the ACM</i>, 1995, 38(11): 39–41. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1145/219717.219748" target="_blank">10.1145/219717.219748</a>. </div> </td> </tr> <tr class="document-box" id="b23"> <td valign="top" class="td1"> [23] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Pilehvar M T, Collier N. De-conflated semantic representations. In <i>Proc</i>. <i>the 2016 Conference on Empirical Methods in Natural Language Processing</i>, Nov. 2016, pp.1680–1690. DOI: <a href="http://dx.doi.org/10.18653/v1/D16-1174">10.18653/v1/D16-1174</a>. </div> </td> </tr> <tr class="document-box" id="b24"> <td valign="top" class="td1"> [24] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Lee Y Y, Yen T Y, Huang H H, Shiue Y T, Chen H H. GenSense: A generalized sense retrofitting model. In <i>Proc</i>. <i>the 27th International Conference on Computational Linguistics</i>, Aug. 2018, pp.1662-1671. </div> </td> </tr> <tr class="document-box" id="b25"> <td valign="top" class="td1"> [25] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Ramprasad S, Maddox J. CoKE: Word sense induction using contextualized knowledge embeddings. In <i>Proc</i>. <i>the 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering</i>, Mar. 2019. </div> </td> </tr> <tr class="document-box" id="b26"> <td valign="top" class="td1"> [26] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Scarlini B, Pasini T, Navigli R. SensEmBERT: Context-enhanced sense embeddings for multilingual word sense disambiguation. <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, 2020, 34(5): 8758–8765. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1609/aaai.v34i05.6402" target="_blank">10.1609/aaai.v34i05.6402</a>. </div> </td> </tr> <tr class="document-box" id="b27"> <td valign="top" class="td1"> [27] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Eyal M, Sadde S, Taub-Tabib H, Goldberg Y. Large scale substitution-based word sense induction. In <i>Proc</i>. <i>the 60th Annual Meeting of the Association for Computational Linguistics</i>, May 2022, pp.4738–4752. DOI: <a href="http://dx.doi.org/10.18653/v1/2022.acl-long.325">10.18653/v1/2022.acl-long.325</a>. </div> </td> </tr> <tr class="document-box" id="b28"> <td valign="top" class="td1"> [28] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Neelakantan A, Shankar J, Passos A, McCallum A. Efficient non-parametric estimation of multiple embeddings per word in vector space. In <i>Proc</i>. <i>the 2014 Conference on Empirical Methods in Natural Language Processing</i>, Oct. 2014, pp.1059–1069. DOI: <a href="http://dx.doi.org/10.3115/v1/D14-1113">10.3115/v1/D14-1113</a>. </div> </td> </tr> <tr class="document-box" id="b29"> <td valign="top" class="td1"> [29] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Pelevina M, Arefiev N, Biemann C, Panchenko A. Making sense of word embeddings. In <i>Proc</i>. <i>the 1st Workshop on Representation Learning for NLP</i>, Aug. 2016, pp.174–183. DOI: <a href="http://dx.doi.org/10.18653/v1/W16-1620">10.18653/v1/W16-1620</a>. </div> </td> </tr> <tr class="document-box" id="b30"> <td valign="top" class="td1"> [30] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Chang H S, Agrawal A, Ganesh A, Desai A, Mathur V, Hough A, McCallum A. Efficient graph-based word sense induction by distributional inclusion vector embeddings. In <i>Proc</i>. <i>the 12th Workshop on Graph-Based Methods for Natural Language Processing</i>, Jun. 2018, pp.38–48. DOI: <a href="http://dx.doi.org/10.18653/v1/W18-1706">10.18653/v1/W18-1706</a>. </div> </td> </tr> <tr class="document-box" id="b31"> <td valign="top" class="td1"> [31] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Han S Z, Shirai K. Unsupervised word sense disambiguation based on word embedding and collocation. In <i>Proc</i>. <i>the 13th International Conference on Agents and Artificial Intelligence</i>, Feb. 2021, pp.1218–1225. DOI: <a href="http://dx.doi.org/10.5220/0010380112181225">10.5220/0010380112181225</a>. </div> </td> </tr> <tr class="document-box" id="b32"> <td valign="top" class="td1"> [32] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Chen H H, Jin H. Finding and evaluating the community structure in semantic peer-to-peer overlay networks. <i>Science China Information Sciences</i>, 2011, 54(7): 1340–1351. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1007/s11432-011-4296-6" target="_blank">10.1007/s11432-011-4296-6</a>. </div> </td> </tr> <tr class="document-box" id="b33"> <td valign="top" class="td1"> [33] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Gao W, Wong K F, Xia Y Q, Xu R F. Clique percolation method for finding naturally cohesive and overlapping document clusters. In <i>Proc</i>. <i>the 21st International Conference on Computer Processing of Oriental Languages</i>, Dec. 2006, pp.97–108. DOI: <a href="http://dx.doi.org/10.1007/11940098_10">10.1007/11940098_10</a>. </div> </td> </tr> <tr class="document-box" id="b34"> <td valign="top" class="td1"> [34] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Gibbons T R, Mount S M, Cooper E D, Delwiche C F. Evaluation of BLAST-based edge-weighting metrics used for homology inference with the Markov Clustering algorithm. <i>BMC Bioinformatics</i>, 2015, 16: 218. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1186/s12859-015-0625-x" target="_blank">10.1186/s12859-015-0625-x</a>. </div> </td> </tr> <tr class="document-box" id="b35"> <td valign="top" class="td1"> [35] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Brin S, Page L. Reprint of: The anatomy of a large-scale hypertextual web search engine. <i>Computer Networks</i>, 2012, 56(18): 3825–3833. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1016/j.comnet.2012.10.007" target="_blank">10.1016/j.comnet.2012.10.007</a>. </div> </td> </tr> <tr class="document-box" id="b36"> <td valign="top" class="td1"> [36] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Yoshua B, Olivier D, Nicolas Le R. Label propagation and quadratic criterion. <i>Semi-Supervised Learning</i>, 2006: 192–216. DOI: <a href="http://dx.doi.org/10.7551/mitpress/9780262033589.003.0011">10.7551/mitpress/9780262033589.003.0011</a>. </div> </td> </tr> <tr class="document-box" id="b37"> <td valign="top" class="td1"> [37] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Serban O, Castellano G, Pauchet A, Rogozan A, Pecuchet J P. Fusion of smile, valence and NGram features for automatic affect detection. In <i>Proc</i>. <i>the 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction</i>, Sept. 2013, pp.264–269. DOI: <a href="http://dx.doi.org/10.1109/ACE285A1.2013.50">10.1109/ACⅡ.2013.50</a>. </div> </td> </tr> <tr class="document-box" id="b38"> <td valign="top" class="td1"> [38] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Jin H, Zhang Z B, Yuan P P. Improving Chinese word representation using four corners features. <i>IEEE Trans. Big Data</i>, 2022, 8(4): 982–993. DOI: <a class="mainColor ref-doi" href="http://dx.doi.org/10.1109/TBDATA.2021.3106582" target="_blank">10.1109/TBDATA.2021.3106582</a>. </div> </td> </tr> <tr class="document-box" id="b39"> <td valign="top" class="td1"> [39] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Huang E H, Socher R, Manning C D, Ng A Y. Improving word representations via global context and multiple word prototypes. In <i>Proc</i>. <i>the 50th Annual Meeting of the Association for Computational Linguistics</i>, Jul. 2012, pp.873-882. </div> </td> </tr> <tr class="document-box" id="b40"> <td valign="top" class="td1"> [40] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Biemann C. Turk bootstrap word sense inventory 2.0: A large-scale resource for lexical substitution. In <i>Proc</i>. <i>the 8th International Conference on Language Resources and Evaluation</i>, May 2012, pp.4038-4042. </div> </td> </tr> <tr class="document-box" id="b41"> <td valign="top" class="td1"> [41] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Pennington J, Socher R, Manning C. GloVe: Global vectors for word representation. In <i>Proc</i>. <i>the 2014 Conference on Empirical Methods in Natural Language Processing</i>, Oct. 2014, pp.1532–1543. DOI: <a href="http://dx.doi.org/10.3115/v1/D14-1162">10.3115/v1/D14-1162</a>. </div> </td> </tr> <tr class="document-box" id="b42"> <td valign="top" class="td1"> [42] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Ilić S, Marrese-Taylor E, Balazs J A, Matsuo Y. Deep contextualized word representations for detecting sarcasm and irony. In <i>Proc</i>. <i>the 9th Workshop on Computational Approaches to Subjectivity</i>, <i>Sentiment and Social Media Analysis</i>, Oct. 2018, pp.2–7. DOI: <a href="http://dx.doi.org/10.18653/v1/w18-6202">10.18653/v1/w18-6202</a>. </div> </td> </tr> <tr class="document-box" id="b43"> <td valign="top" class="td1"> [43] </td> <td class="td2"> <div class="reference-en" style="margin:0px;padding:0px;"> Liu Y J, Che W X, Wang Y X, Zheng B, Qin B, Liu T. Deep contextualized word embeddings for universal dependency parsing. <i>ACM Trans</i>. <i>Asian and Low-Resource Language Information Processing</i>, 2020, 19(1): 9. DOI: <a href="http://dx.doi.org/10.1145/3326497">10.1145/3326497</a>. </div> </td> </tr> </tbody> </table>
[1] Fu-Rong Dang, Jin-Tao Tang, Kun-Yuan Pang, Ting Wang, Sha-Sha Li, Xiao Li. Constructing an Educational Knowledge Graph with Concepts Linked to Wikipedia [J]. Journal of Computer Science and Technology, 2021, 36(5): 1200-1211.
[2] Xiang-Guang Zhou, Ren-Bin Gong, Fu-Geng Shi, Zhe-Feng Wang. PetroKG: Construction and Application of Knowledge Graph in Upstream Area of PetroChina [J]. Journal of Computer Science and Technology, 2020, 35(2): 368-378.
[3] Ji-Zhao Zhu, Yan-Tao Jia, Jun Xu, Jian-Zhong Qiao, Xue-Qi Cheng. Modeling the Correlations of Relations for Knowledge Graph Embedding [J]. , 2018, 33(2): 323-334.
[4] Ze-Qi Lin, Bing Xie, Yan-Zhen Zou, Jun-Feng Zhao, Xuan-Dong Li, Jun Wei, Hai-Long Sun, Gang Yin. Intelligent Development Environment and Software Knowledge Graph [J]. , 2017, 32(2): 242-249.
[5] Fei Tian, Bin Gao, En-Hong Chen, Tie-Yan Liu. Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph [J]. , 2016, 31(3): 624-634.
[6] Javier Tejada-Cárcamo, Hiram Calvo, Alexander Gelbukh, and Kazuo Hara. Unsupervised WSD by Finding the Predominant Sense Using Context as a Dynamic Thesaurus [J]. , 2010, 25(5): 1030-1039.
[7] Chaveevan Pechsiri and Rapepun Piriyakul. Explanation Knowledge Graph Construction Through Causality Extraction from Texts [J]. , 2010, 25(5): 1055-1070.
[8] Sheng Li and Tie-Jun Zhao. Chinese Information Processing and Its Prospects [J]. , 2006, 21(5): 838-846 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] Zhou Di;. A Recovery Technique for Distributed Communicating Process Systems[J]. , 1986, 1(2): 34 -43 .
[2] Li Wei;. A Structural Operational Semantics for an Edison Like Language(2)[J]. , 1986, 1(2): 42 -53 .
[3] Li Wanxue;. Almost Optimal Dynamic 2-3 Trees[J]. , 1986, 1(2): 60 -71 .
[4] Liu Mingye; Hong Enyu;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[5] C.Y.Chung; H.R.Hwa;. A Chinese Information Processing System[J]. , 1986, 1(2): 15 -24 .
[6] Sun Zhongxiu; Shang Lujun;. DMODULA:A Distributed Programming Language[J]. , 1986, 1(2): 25 -31 .
[7] Chen Shihua;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[8] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[9] Jin Lan; Yang Yuanyuan;. A Modified Version of Chordal Ring[J]. , 1986, 1(3): 15 -32 .
[10] Pan Qijing;. A Routing Algorithm with Candidate Shortest Path[J]. , 1986, 1(3): 33 -52 .

ISSN 1000-9000(Print)

         1860-4749(Online)
CN 11-2296/TP

Home
Editorial Board
Author Guidelines
Subscription
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
Tel.:86-10-62610746
E-mail: jcst@ict.ac.cn
 
  Copyright ©2015 JCST, All Rights Reserved