|本期目录/Table of Contents|

[1]魏庭新,曲维光*,宋 丽,等.面向中文抽象语义表示的复句研究综述[J].厦门大学学报(自然科学版),2018,57(06):849-858.[doi:10.6043/j.issn.0438-0479.201805011]
 WEI Tingxin,QU Weiguang*,SONG Li,et al.A Survey on the Study of Compound Sentences with Chinese Abstract Meaning Representation[J].Journal of Xiamen University(Natural Science),2018,57(06):849-858.[doi:10.6043/j.issn.0438-0479.201805011]
点击复制

面向中文抽象语义表示的复句研究综述(PDF/HTML)
分享到:

《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
57卷
期数:
2018年06期
页码:
849-858
栏目:
自然语言处理
出版日期:
2018-11-28

文章信息/Info

Title:
A Survey on the Study of Compound Sentences with Chinese Abstract Meaning Representation
文章编号:
0438-0479(2018)06-0849-10
作者:
魏庭新12曲维光234*宋 丽2戴茹冰2
1.南京师范大学国际文化教育学院,2.南京师范大学文学院,3.南京师范大学计算机科学与技术学院,江苏 南京 210097; 4.福建省信息处理与智能控制重点实验室(闽江学院),福建 福州 350121
Author(s):
WEI Tingxin12QU Weiguang234*SONG Li2DAI Rubing2
1.International College for Chinese Studies,2.School of Chinese Language and Literature,3.School of Computer Science and Technology,Nanjing Normal University,Nanjing 210097,China; 4.Fujian Provincial Key Laboratory of Information Processing and Intellig
关键词:
中文抽象语义表示 复句 篇章关系
Keywords:
Chinese abstract meaning representation(CAMR) compound sentence discourse relation
分类号:
TP 391.1
DOI:
10.6043/j.issn.0438-0479.201805011
文献标志码:
A
摘要:
抽象语义表示(AMR)是一种新型的句子语义表示方式.中文AMR在英文AMR的基础上,针对汉语特点,增加了复句逻辑语义关系的表示.中文AMR以句子为基本标注单位,以层次结构树形式表示各分句间的逻辑关系.由于允许论元共享,因此在树结构基础上形成图结构,从而对复句的语义表示更加完整全面.为了进一步研究中文AMR,对目前复句关系研究现状、复句及篇章关系资源的建设进行了综述,指出目前研究存在的问题,并提出将来工作研究的方向.
Abstract:
Abstract meaning representation(AMR)is a novel framework of representing sentential meaning.Due to linguistic characteristics of the Chinese language,Chinese AMR(CAMR)annotates the semantic compound-sentence meaning,which is ignored in English AMR annotation.Sentence is the elementary unit of CAMR and hierarchical tree structure is used to represent the logical relations of all minimal sentences in compound sentences.As arguments can be shared,the graph based on the tree structure expresses the semantic meaning of compound sentences more comprehensively.This paper introduces the current resource construction and methodology of compound sentence relations and discourse relations,as well as points out the key problems lying in present studies.Then future work is discussed.

参考文献/References:

[1] BANARESCU L,BONIAL C,CAI S,et al.Abstract meaning representation for sembanking[C]∥Linguistic Annotation Workshop and Interoperability with Discourse.Sofia:Bulgaria,2013:178-186.
[2] 曲维光,周俊生,吴晓东,等.自然语言句子抽象语义表示AMR研究综述[J].数据采集与处理,2017,32(1):26-36.
[3] 周祖谟.现代汉语讲座[M].北京:知识出版社,1983:154-167.
[4] 胡金柱,舒江波,胡泉,等.复句关系词自动识别中规则的表示方法研究[J].计算机工程与应用,2016,52(1):127-132.
[5] MANN W C,THOMPSON S A.Rhetorical structure theory:toward a functional theory of text organization[J].Text,1988,8(3):243-281.
[6] 徐赳赳,WEBSTER J.复句研究与修辞结构理论[J].外语教学与研究,1999(4):16-22.
[7] 黄伯荣,廖序东.现代汉语(增订版)[M].北京:高等教育出版社,2002:123-133.
[8] 邢福义.汉语复句研究[M].北京:商务印书馆,2001:38-47.
[9] 胡明扬,劲松.流水句初探[J].语言教学与研究,1989(4):42-54.
[10] PRASAD R,DINESH N,LEE A,et al.The Penn Discourse TreeBank 2.0[C]∥International Conference on the 6th Language Resources and Evaluation.Marrakech,Morocco:LREC,2008:2961-2968.
[11] KNIGHT K,BADARAU B,BARANESCU L,et al.Abstract meaning representation(AMR)annotation release 2.0 [DB/OL].[2017-06-15].https:∥catalog.ldc.upenn.edu/LDC2017T10.
[12] 王力.中国语法理论[M].北京:商务印书馆,1951:39-40.
[13] LI B,WEN Y,QU W G,et al.Annotating the little prince with Chinese AMRs[C]∥Linguistic Annotation Workshop Held in Conjunction with ACL.Berlin:ACL,2016:7-15.
[14] ZHOU Y,XUE N.PDTB-style discourse annotation of Chinese text[C]∥Meeting of the Association for Computational Linguistics:Long Papers.Jeju Island:ACL,2012:69-77.
[15] ZHOU Y,XUE N.The Chinese discourse treebank:a Chinese corpus annotated with discourse relations[J].Language Resource and Evaluation,2015,49(2):397-431
[16] 周强.汉语句法树库标注体系[J].中文信息学报,2004,18(4):2-9.
[17] 李艳翠.汉语篇章结构表示体系及资源构建研究[D].苏州:苏州大学,2015:65-80.
[18] CARLSON L,MARCU D,OKUROWSKI M E.Building a discourse-tagged corpus in the framework of rhetorical structure theory[J].Springer Netherlands,2003,18(18):2655-2661.
[19] 邢福义,姚双云.汉语复句语料库的建设与利用[C]∥第三届HNC与语言学研究学术研讨会论文集.北京:北京师范大学出版社,2005:432-437.
[20] 李斌,闻媛,卜丽君,等.英汉《小王子》抽象语义图结构的对比分析[J].中文信息学报,2017,31(1):50-57.
[21] 张牧宇,秦兵,刘挺.中文篇章关系任务分析及语料标注[J].智能计算机与应用,2016,6(5):1-4.
[22] SORICUT R,MARCU D.Sentence level discourse parsing using syntactic and lexical information[C]∥Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology.Association for Computational Linguistics.Edmonton:ACL,149-156.
[23] HERNAULT H,PRENDINGER H,DUVERLE D A,et al.HILDA:a discourse parser using support vector machine classification[J].Dialogue and Discourse,2010,1(3):1-33.
[24] LIN Z,NG H T,KAN M Y.A PDTB-styled end-to-end discourse parser[J].Natural Language Engineering,2014,20(2):151-184.
[25] 洪鹿平.汉语复句关系自动判定研究[D].南京:南京师范大学,2008:13-31.
[26] 胡金柱,俞小娟,李琼,等.基于规则库和聚类分析的复句短语字段的自动识别研究[J].华中师范大学学报(自科版),2008,42(2):190-194.
[27] PITLER E,RAGHUPATHY M,MEHTA H,et al.Easily identifiable discourse relations[C]∥International Conference on Computational Linguistics,Posters Proceedings.Manchester:ICCL,2008:87-90.
[28] PITLER E,NENKOVA A.Using syntax to disambiguate explicit discourse connectives in text[C]∥Proceedings of the Association for Computational Linguistics and AFNLP.Singapore:ACL,2009:13-16.
[29] 李艳翠,孙静,周国栋,等.基于清华汉语树库的复句关系词识别与分类研究[J].北京大学学报(自然科学版),2014,50(1):118-124.
[30] 胡金柱,吴锋文,李琼,等.汉语复句关系词库的建设及其利用[J].语言科学,2010,9(2):133-142.
[31] 李艳翠,孙静,周国栋.汉语篇章连接词识别与分类[J].北京大学学报(自然科学版),2015,51(2):307-314.
[32] 杨进才,郭凯凯,沈显君,等.基于贝叶斯模型的复句关系词自动识别与规则挖掘[J].计算机科学,2015,42(7):291-294.
[33] 张牧宇,宋原,秦兵,等.中文篇章级句间语义关系识别[J].中文信息学报,2013,27(6):51-58.
[34] 杨进才,陈忠忠,沈显君,等.二句式非充盈态有标复句关系类别的自动标志[J].计算机应用研究,2017,34(10):2950-2953.
[35] MARCU D,ECHIHABI A.An unsupervised approach to recognizing discourse relations[C]∥Meeting on Association for Computational Linguistics.Association for Computational Linguistics.Philadelphia:ACL,2002:368-375.
[36] PITLER E,LOUIS A,NENKOVA A.Automatic sense prediction for implicit discourse relations in text[C]∥Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AfNLP.Singapore:ACL,2009:683-691.
[37] LIN Z,KAN M Y,NG H T.Recognizing implicit discourse relations in the Penn discourse treebank[C]∥Conference on Empirical Methods in Natural Language Processing.Singapore:EMNLP,2009:343-351.
[38] LOUIS A,JOSHI A,PRASAD R,et al.Using entity features to classify implicit discourse relations[C]∥Meeting of the Special Interest Group on Discourse and Dialogue.Association for Computational Linguistics.Tokyo:ACL,2010:59-62.
[39] RUTHERFORD A T,XUE N.Discovering implicit discourse relations through Brown cluster pair representation and coreference patterns[C]∥Conference of the European Chapter of the Association for Computational Linguistics.Gothenburg:ACL,2014:645-654.
[40] RUTHERFORD A,XUE N.Improving the inference of implicit discourse relations via classifying explicit discourse connectives[C]∥Conference of the North American Chapter of the Association for Computational Linguistics.Denvor:ACL,2015:799-808.
[41] 车婷婷,洪宇,周小佩,等.基于功能连接词的隐式篇章关系推理[J].中文信息学报,2014,28(2):17-27.
[42] 孙静,李艳翠,周国栋,等.汉语隐式篇章关系识别[J].北京大学学报(自然科学版),2014,50(1):111-117.
[43] 李国臣,张雅星,李茹.基于汉语框架语义网的篇章关系识别[J].中文信息学报,2017,31(6):172-179.
[44] JI Y,EISENSTEIN J.Entity-augmented distributional semantics for discourse relations[J].Transaction for computational Linguistics(TACL),2014,3:329-344.
[45] ZHANG B,SU J,XIONG D,et al.Shallow convolutional neural network for implicit discourse relation recognition[C]∥Conference on Empirical Methods in Natural Language Processing.Lisbon:EMNLP,2015:2230-2235.
[46] LIU Y,LI S.Recognizing implicit discourse relations via repeated reading:neural networks with multi-level attention[C]∥Conference on Empirical Methods in Natural Language Processing.Austin:EMNLP,2016:1224-1233.
[47] LI H,ZHANG J,ZONG C.Implicit discourse relation recognition for English and Chinese with multiview modeling and effective representation learning[J].ACM Trans Asian Low-Resour Lang Inf Process,2017,16(3):1-21.
[48] QIN L,ZHANG Z,ZHAO H,et al.Adversarial connective-exploiting networks for implicit discourse relation classification[EB/OL].[2017-04-01].http:∥cn.arxiv.org/abs/1704.00217.
[49] GENG R,JIAN P,ZHANG Y,et al.Implicit discourse relation identification based on tree structure neural network[C]∥International Conference on Asian Language Processing.Singapore:IEEE,2017:334-337.
[50] WANG Y,LI S,YANG J,et al.Tag-enhanced tree-structured neural networks for implicit discourse relation classification[C]∥International Joint Conference on Natural Language Processing.Taipei:AFNLP,2017:496-505.
[51] DAI Z,HUANG R.Improving implicit discourse relation classification by modeling inter-dependencies of discourse units in a paragraph[C]∥the North American Chapter of the Association for Computational Linguistics.New Orleans:ACL,2018,June 1-6.
[52] LETHANH H,ABEYSINGHE G,HUYCK C.Generating discourse structures for written texts[C]∥Proceedings of International Conference on Computational Linguistics.Association for Computational Linguistics.London:ACL,2004:329-335.
[53] 张益民,陆汝占.一种混合型的汉语篇章结构自动分析方法[J].软件学报,2000,11(11):1527-1533.
[54] 涂眉,周玉,宗成庆.基于最大熵的汉语篇章结构自动分析方法[J].北京大学学报(自然科学版),2014,50(1):125-132.

备注/Memo

备注/Memo:
收稿日期:2018-05-09 录用日期:2018-10-04
基金项目:国家自然科学基金(61772278,61472191); 福建省信息处理与智能控制重点实验室开放基金(MJUKF201705)
*通信作者:wgqu_nj@163.com
引文格式:魏庭新,曲维光,宋丽,等.面向中文抽象语义表示的复句研究综述[J].厦门大学学报(自然科学版),2018,57(6):849-858.
Citation:WEI T X,QU W G,SONG L,et al.A survey on the study of compound sentences with Chinese abstract meaning representation[J].J Xiamen Univ Nat Sci,2018,57(6):849-858.(in Chinese)
更新日期/Last Update: 1900-01-01