地名实体识别研究与展望

doi:10.3778/j.issn.1002-8331.2302-0381

摘要/Abstract

摘要： 地名作为一种常见的命名实体，广泛存在于非结构化文本中。是非结构化数据转为结构化过程中重要的关联实体。为了全面了解地名识别的最新研究成果和现状，概述了地名识别现有的应用场景、地名识别技术在具体场景的详细应用以及地名识别数据集和评价指标。总结分析了现有的地名识别方法：基于规则和地名词典匹配的方法、基于统计机器学习的方法、基于深度学习模型和混合模型方法。归纳总结了每一种地名识别方法的关键思路、优缺点和具体模型。同时对混合方法的融合特征和模型特点进行了总结归纳。并从模型性能展开比对分析，以及对词嵌入模型和预训练模型的模型特点进行了总结归纳。对地名实体识别研究方向进行总结和展望。

关键词: 命名实体识别, 地名实体识别, 自然语言处理, 深度学习, 信息抽取

Abstract: As a common naming entity, place names are widely used in unstructured texts. It is an important associated entity in the process of transforming unstructured data into structured data. In order to fully understand the latest research results and status quo of place name recognition. Firstly, the existing application scenarios of place name recognition, detailed application of place name recognition technology in specific scenarios, data sets and evaluation indicators of place name recognition are summarized. Then it summarizes and analyzes the existing methods of place name recognition. The method based on rules and place name dictionary matching, the method based on statistical machine learning, the method based on deep learning model and the mixed model method. The key ideas, advantages and disadvantages and specific models of each method are summarized. At the same time, the fusion characteristics and model characteristics of the hybrid method are summarized. The model performance is compared and analyzed. The characteristics of the word embedding model and the pre-training model are summarized. Finally, the research direction of place name entity recognition is summarized and prospected.

Key words: named entity recognition, toponym entity recognition, natural language processing, deep learning, information extraction

王文涛, 奚雪峰, 崔志明, 徐川. 地名实体识别研究与展望[J]. 计算机工程与应用, 2023, 59(21): 66-82.

WANG Wentao, XI Xuefeng, CUI Zhiming, XU Chuan. Research and Prospect of Toponym Entity Recognition[J]. Computer Engineering and Applications, 2023, 59(21): 66-82.

参考文献

[1] DUTT R，BASU M，GHOSH K，et al.Utilizing microblogs for assisting post-disaster relief operations via matching resource needs and availabilities[J].Information Processing & Management，2019，56（5）：1680-1697.
[2] BELCASTRO L，MAROZZO F，TALIA D，et al.Using social media for sub-event detection during disasters[J].Journal of Big Data，2021，8（1）：1-22.
[3] FAN C，WU F，MOSTAFAVI A.A hybrid machine learning pipeline for automated mapping of events and locations from social media in disasters[J].IEEE Access，2020，8：10478-10490.
[4] MAO H，THAKUR G，SPARKS K，et al.Mapping near-real-time power outages from social media[J].International Journal of Digital Earth，2019，12（11）：1285-1299.
[5] TATEOSIAN L，GUENTER R，YANG Y P，et al.Tracking 19th century late blight from archival documents using text analytics and geoparsing[C]//International Conference for Free and Open Source Software for Geospatial（FOSS4G），2017：17.
[6] KAMALLOO E，RAFIEI D.A coherent unsupervised model for toponym resolution[C]//Proceedings of the 2018 World Wide Web Conference，2018：1287-1296.
[7] WANG J，HU Y，JOSEPH K.NeuroTPR：a neuro‐net toponym recognition model for extracting locations from social media messages[J].Transactions in GIS，2020，24（3）：719-735.
[8] MARTíNEZ N J F，PERI?áN-PASCUAL C.Knowledge-based rules for the extraction of complex，fine-grained locative references from tweets[J].RAEL：Revista Electrónica de LingüíStica Aplicada，2020，19（1）：136-163.
[9] RITTER A，CLARK S，ETZIONI O.Named entity recognition in tweets：an experimental study[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing，2011.
[10] QIU Q，XIE Z，WU L，et al.DGeoSegmenter：a dictionary-based Chinese word segmenter for the geoscience domain[J].Computers & Geosciences，2018，121：1-11.
[11] MA K，TAN Y，XIE Z，et al.Chinese toponym recognition with variant neural structures from social media messages based on BERT methods[J].Journal of Geographical Systems，2022，24（2）：143-169.
[12] LIEBERMAN M D，SAMET H.Multifaceted toponym recognition for streaming news[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval，2011：843-852.
[13] GELERNTER J，ZHANG W.Cross-lingual geo-parsing for non-structured data[C]//Proceedings of the 7th Workshop on Geographic Information Retrieval，2013：64-71.
[14] LEIDNER J L，LIEBERMAN M D.Detecting geographical references in the form of place names and associated spatial natural language[J].Sigspatial Special，2011，3（2）：5-11.
[15] GIRIDHAR P，ABDELZAHER T，GEORGE J，et al.On quality of event localization from social network feeds[C]//2015 IEEE International Conference on Pervasive Computing and Communication Workshops（PerCom Workshops），2015：75-80.
[16] ALAM F，SAJJAD H，IMRAN M，et al.CrisisBench：benchmarking crisis-related social media datasets for humanitarian information processing[C]//The International AAAI Conference on Web and Social Media，2021：923-932.
[17] ANDREADIS S，ANTZOULATOS G，MAVROPOULOS T，et al.A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets[J].Online Social Networks and Media，2021，23：100134.
[18] SILVA M J，MARTINS B，CHAVES M，et al.Adding geographic scopes to web resources[J].Computers，Environment and Urban Systems，2006，30（4）：378-399.
[19] MIKOLOV T，CHEN K，CORRADO G，et al.Efficient estimation of word representations in vector space[J].arXiv：1301.3781，2013.
[20] CLOUGH P.Extracting metadata for spatially-aware information retrieval on the internet[C]//Proceedings of the 2005 Workshop on Geographic Information Retrieval，2005：25-30.
[21] POULIQUEN B，KIMLER M，STEINBERGER R，et al.Geocoding multilingual texts：recognition，disambiguation and visualisation[J].arXiv：cs/0609065，2006.
[22] PARADESI S M.Geotagging tweets using their content[C]//Twenty-Fourth International FLAIRS Conference，2011.
[23] WOODRUFF A G，PLAUNT C.GIPSY：automated geographic indexing of text documents[J].Journal of the American Society for Information Science，1994，45（9）：645-655.
[24] TAMAMES J，DE LORENZO V.EnvMine：a text-mining system for the automatic extraction of contextual information[J].BMC Bioinformatics，2010，11（1）：1-10.
[25] FERRAGINA P，SCAIELLA U.Tagme：on-the-fly annotation of short text fragments（by wikipedia entities）[C]//Proceedings of the 19th ACM International Conference on Information and Knowledge Management，2010：1625-1628.
[26] AMITAY E，HAR'EL N，SIVAN R，et al.Web-a-where：geotagging web content[C]//Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval，2004：273-280.
[27] AKBAR S.Table extraction from web pages using conditional random fields to extract toponym related data[C]//Journal of Physics：Conference Series，2017：012064.
[28] DE BRUIJN J A，DE MOEL H，JONGMAN B，et al.TAGGS：grouping tweets to improve global geoparsing for disaster response[J].Journal of Geovisualization and Spatial Analysis，2018，2（1）：1-14.
[29] AL-OLIMAT H S，THIRUNARAYAN K，SHALIN V，et al.Location name extraction from targeted text streams using gazetteer-based statistical language models[J].arXiv：1708.03105，2017.
[30] MILUSHEVA S，MARTY R，BEDOYA G，et al.Applying machine learning and geolocation techniques to social media data（Twitter） to develop a resource for urban planning[J].PloS One，2021，16（2）：e0244317.
[31] MILLEVILLE K，VERSTOCKT S，VAN DE WEGHE N.Improving toponym recognition accuracy of historical topographic maps[C]//International Workshop on Automatic Vectorisation of Historical Maps，2020.
[32] AHMED M F，VANAJAKSHI L，SURIYANARAYANAN R.Real-time traffic congestion information from tweets using supervised and unsupervised machine learning techniques[J].Transportation in Developing Economies，2019，5（2）：1-11.
[33] ALI F，ALI A，IMRAN M，et al.Traffic accident detection and condition analysis based on social networking data[J].Accident Analysis & Prevention，2021，151：105973.
[34] 罗凌，杨志豪，宋雅文，等.基于笔画ELMo和多任务学习的中文电子病历命名实体识别研究[J].计算机学报，2020，43（10）：1943-1957.
LUO L，YANG Z H，SONG Y W，et al.Chinese clinical named entity recognition based on stroke ELMo and multi-task learning[J].Chinese Journal of Computers，2020，43（10）：1943-1957.
[35] FIZE J，MONCLA L，MARTINS B.Deep learning for toponym resolution：geocoding based on pairs of toponyms[J].ISPRS International Journal of Geo-Information，2021，10（12）：818.
[36] UNANKARD S，LI X，SHARAF M A.Emerging event detection in social networks with location sensitivity[J].World Wide Web，2015，18（5）：1393-1417.
[37] GELERNTER J，MUSHEGIAN N.Geo‐parsing messages from microtext[J].Transactions in GIS，2011，15（6）：753-773.
[38] LINGAD J，KARIMI S，YIN J.Location extraction from disaster-related microblogs[C]//Proceedings of the 22nd International Conference on World Wide Web，2013：1017-1020.
[39] GUTIERREZ C，FIGUERIAS P，OLIVEIRA P，et al.Twitter mining for traffic events detection[C]//2015 Science and Information Conference（SAI），2015：371-378.
[40] KARIMZADEH M，PEZANOWSKI S，MACEACHREN A M，et al.GeoTxt：a scalable geoparsing system for unstructured text geolocation[J].Transactions in GIS，2019，23（1）：118-136.
[41] MIRCEA A.Real-time classification，geolocation and interactive visualization of COVID-19 information shared on social media to better understand global developments[C]//Proceedings of the 1st Workshop on NLP for COVID-19（Part 2） at EMNLP 2020，2020.
[42] SUAT-ROJAS N，GUTIERREZ-OSORIO C，PEDRAZA C.Extraction and analysis of social networks data to detect traffic accidents[J].Information，2022，13（1）：26.
[43] RATINOV L，ROTH D.Design challenges and misconceptions in named entity recognition[C]//Proceedings of the Thirteenth Conference on Computational Natural Language Learning（CoNLL-2009），2009：147-155.
[44] BONTCHEVA K，DERCZYNSKI L，FUNK A，et al.Twitie：an open-source information extraction pipeline for microblog text[C]//Proceedings of the International Conference Recent Advances in Natural Language Processing，2013：83-90.
[45] MANNING C D，SURDEANU M，BAUER J，et al.The Stanford CoreNLP natural language processing toolkit[C]//Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics：System Demonstrations，2014：55-60.
[46] HABIB M B，KEULEN M V.A hybrid approach for robust multilingual toponym extraction and disambiguation[C]//Intelligent Information Systems Symposium，2013：1-15.
[47] SOBHANA N，MITRA P，GHOSH S.Conditional random field based named entity recognition in geological text[J].International Journal of Computer Applications，2010，1（3）：143-147.
[48] WEISSENBACHER D，SARKER A，TAHSIN T，et al.Extracting geographic locations from the literature for virus phylogeography using supervised and distant supervision methods[J].AMIA Summits on Translational Science Proceedings，2017，2017：114-122.
[49] WEINMAN J.Geographic and style models for historical map alignment and toponym recognition[C]//2017 14th IAPR International Conference on Document Analysis and Recognition（ICDAR），2017：957-964.
[50] SULTANIK E A，FINK C.Rapid geotagging and disambiguation of social media text via an indexed gazetteer[C]//International Conference on Information Systems for Crisis Response and Management，2012.
[51] NISSIM M，MATHESON C，REID J.Recognising geographical entities in Scottish historical documents[C]//Proceedings of the Workshop on Geographic Information Retrieval at SIGIR 2004，2004.
[52] SAGCAN M，KARAGOZ P.Toponym recognition in social media for estimating the location of events[C]//2015 IEEE International Conference on Data Mining Workshop（ICDMW），2015：33-39.
[53] CURRAN J R，CLARK S.Language independent NER using a maximum entropy tagger[C]//Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003，2003：164-167.
[54] CARPENTER B.Character language models for Chinese word segmentation and named entity recognition[C]//Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing，2006：169-172.
[55] WANG X，MA C，ZHENG H，et al.Dm_nlp at semeval-2018 task 12：a pipeline system for toponym resolution[C]//Proceedings of the 13th International Workshop on Semantic Evaluation，2019：917-923.
[56] LAFFERTY J D，MCCALLUM A，PEREIRA F C N.Conditional random fields：probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the Eighteenth International Conference on Machine Learning，2001：282-289.
[57] LAMPLE G，BALLESTEROS M，SUBRAMANIAN S，et al.Neural architectures for named entity recognition[J].arXiv：1603.01360，2016.
[58] YADAV V，BETHARD S.A survey on recent advances in named entity recognition from deep learning models[J].arXiv：1910.11470，2019.
[59] MERRYTON A R，AUGASTA G.A survey on recent advances in machine learning techniques for fake news detection[J].Test Engineering and Management，2020，83：11572-11582.
[60] NASIR J A，KHAN O S，VARLAMIS I.Fake news detection：a hybrid CNN-RNN based deep learning approach[J].International Journal of Information Management Data Insights，2021，1（1）：100007.
[61] LI J，SUN A，HAN J，et al.A survey on deep learning for named entity recognition[J].IEEE Transactions on Knowledge and Data Engineering，2020，34（1）：50-70.
[62] LIMSOPATHAM N，COLLIER N.Bidirectional LSTM for named entity recognition in twitter messages[C]//Proceedings of the 2nd Workshop on Noisy User-generated Text（WNUT），2016：145-152.
[63] AKBIK A，BLYTHE D，VOLLGRAF R.Contextual string embeddings for sequence labeling[C]//Proceedings of the 27th International Conference on Computational Linguistics，2018：1638-1649.
[64] QI Peng，ZHANG Yuhao，ZHANG Yuhui，et al.Stanza：a python natural language processing toolkit for many human languages[J].arXiv：2003.07082，2020.
[65] USHIO A，CAMACHO-COLLADOS J.T-NER：an all-round python library for transformer-based named entity recognition[J].arXiv：2209.12616，2022.
[66] GRITTA M，PILEHVAR M T，COLLIER N.Which melbourne? augmenting geocoding with maps[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics（Volume 1：Long Papers），2018：1285-1296.
[67] KUMAR A，SINGH J P.Location reference identification from tweets during emergencies：a deep learning approach[J].International Journal of Disaster Risk Reduction，2019，33：365-375.
[68] HOCHREITER S，SCHMIDHUBER J.Long short-term memory[J].Neural Computation，1997，9（8）：1735-1780.
[69] XU C，LI J，LUO X，et al.Dlocrl：a deep learning pipeline for fine-grained location recognition and linking in tweets[C]//The World Wide Web Conference，2019：3391-3397.
[70] LI C，SUN A.Fine-grained location extraction from tweets with temporal awareness[C]//Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval，2014：43-52.
[71] SUTSKEVER I，VINYALS O，LE Q V.Sequence to sequence learning with neural networks[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems，2014：3104-3112.
[72] SHEN Y，HEACOCK L，ELIAS J，et al.ChatGPT and other large language models are double-edged swords[J].Radiology，2023，307（2）：230163.
[73] KUNG T H，CHEATHAM M，MEDENILLA A，et al.Performance of ChatGPT on USMLE：potential for aiassisted medical education using large language models[J].PLOS Digital Health，2023，2（2）：e0000198.
[74] BAHDANAU D，CHO K，BENGIO Y.Neural machine translation by jointly learning to align and translate[J].arXiv：1409.0473，2014.
[75] CHO K，VAN MERRI?NBOER B，GULCEHRE C，et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[J].arXiv：1406. 1078，2014.
[76] RANATHUNGA S，LEE E S A，PRIFTI SKENDULI M，et al.Neural machine translation for low-resource languages：a survey[J].ACM Computing Surveys，2023，55（11）：1-37.
[77] HENDY A，ABDELREHIM M，SHARAF A，et al.How good are gpt models at machine translation? a comprehensive evaluation[J].arXiv：2302.09210，2023.
[78] VINYALS O，KAISER ?，KOO T，et al.Grammar as a foreign language[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems，2015.
[79] NING Z，ZHANG Z，SUN T，et al.An empirical study of model errors and user error discovery and repair strategies in natural language database queries[C]//Proceedings of the 28th International Conference on Intelligent User Interfaces（IUI’23），2023.
[80] BI L.Resolving syntactic and semantic ambiguities from a minimalist approach：a case study of mandarin PPs[C]//Proceedings of the World Conference on Intelligent and 3-D Technologies（WCI3DT 2022） Methods，Algorithms and Applications.Singapore：Springer Nature Singapore，2023：197-205.
[81] VASWANI A，SHAZEER N，PARMAR N，et al.Attention is all you need[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems，2017.
[82] DEVLIN J，CHANG M W，LEE K，et al.Bert：pre-training of deep bidirectional transformers for language understanding[J].arXiv：1810.04805，2018.
[83] DAVARI M.Neural network approaches to medical toponym recognition[D].Concordia University，2020.
[84] QIU Q，XIE Z，WANG S，et al.ChineseTR：a weakly supervised toponym recognition architecture based on automatic training data generator and deep neural network[J].Transactions in GIS，2022，26（3）：1256-1279.
[85] CADOREL L，BLANCHI A，TETTAMANZI A G.Geospatial knowledge in housing advertisements：capturing and extracting spatial information from text[C]//Proceedings of the 11th on Knowledge Capture Conference，2021：41-48.
[86] MARTIN L，MULLER B，SUáREZ P J O，et al.CamemBERT：a tasty French language model[J].arXiv：1911. 03894，2019.
[87] CHEN Z，POKHAREL B，LI B，et al.Location extraction from twitter messages using a bidirectional long short-term memory neural network with conditional random field model[C]//International Conference on Geographical Information Systems Theory，Applications and Management，2020：18-30.
[88] MONTEIRO B R，DAVIS JR C A，FONSECA F.A survey on the geographic scope of textual documents[J].Computers & Geosciences，2016，96：23-34.
[89] FREIRE N，BORBINHA J，CALADO P，et al.A metadata geoparsing system for place name recognition and resolution in metadata records[C]//Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries，2011：339-348.
[90] HOANG T B N，MOTHE J.Location extraction from tweets[J].Information Processing & Management，2018，54（2）：129-144.
[91] FERNáNDEZ N J，PERI?áN-PASCUAL C.nLORE：a linguistically rich deep-learning system for locative-reference extraction in tweets[C]//Proceedings of the 17th International Conference on Intelligent Environments，2021：243.
[92] INKPEN D，LIU J，FARZINDAR A，et al.Location detection and disambiguation from twitter messages[J].Journal of Intelligent Information Systems，2017，49（2）：237-253.
[93] MAGGE A，WEISSENBACHER D，SARKER A，et al.Deep neural networks and distant supervision for geographic location mention extraction[J].Bioinformatics，2018，34（13）：565-573.
[94] PETERSON K S，LEWIS J，PATTERSON O V，et al.Automated travel history extraction from clinical notes for informing the detection of emergent infectious disease events：algorithm development and validation[J].JMIR Public Health and Surveillance，2021，7（3）：e26719.
[95] HU X，AL-OLIMAT H S，KERSTEN J，et al.GazPNE：annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules[J].International Journal of Geographical Information Science，2022，36（2）：310-337.
[96] HU X，ZHOU Z，SUN Y，et al.GazPNE2：a general place name extractor for microblogs fusing gazetteers and pretrained transformer models[J].IEEE Internet of Things Journal，2022，9（17）：16259-16271.
[97] NGUYEN D Q，VU T，NGUYEN A T.BERTweet：a pre-trained language model for English Tweets[J].arXiv：2005.10200，2020.
[98] LIU Y，LUO X，TAO Z.Construction of a high-precision general geographical location words dataset[J].Computer Standards & Interfaces，2023，84：103692.
[99] ARDANUY M C，BEAVAN D，BEELEN K，et al.A dataset for toponym resolution in nineteenth-century English newspapers[J].Journal of Open Humanities Data，2022，8（2）.
[100] NOAEEN M，AMINI S，BHASKER S，et al.Unlocking the power of EHRs：harnessing unstructured data for machine learning-based outcome predictions[J].Robotics & Machine Learning Daily News，2023（14）：129-130.
[101] LIU Z，JANOWICZ K，CAI L，et al.Geoparsing：solved or biased? an evaluation of geographic biases in geoparsing[J].AGILE：GIScience Series，2022，3：9.
[102] ZOU L，LIAO D，LAM N S N，et al.Social media for emergency rescue：an analysis of rescue requests on twitter during hurricane harvey[J].International Journal of Disaster Risk Reduction，2023，85：103513.
[103] ZHOU B，ZOU L，HU Y，et al.TopoBERT：plug and play toponym recognition module harnessing fine-tuned BERT[J].arXiv：2301.13631，2023.
[104] 邹恩岑，曾诚，张谦，等.一种面向中文非标建筑地址标准化的自动匹配方法[J].苏州科技大学学报（自然科学版），2019，36（4）：66-74.
ZOU E C，ZENG C，ZHANG Q，et al.An automatic matching approach to standardizing nonstandard Chinese building addresses[J].Journal of Suzhou University of Science and Technology（Natural Science），2019，36（4）：66-74.
[105] HU X，SUN Y，KERSTEN J，et al.How can voting mechanisms improve the robustness and generalizability of toponym disambiguation?[J].International Journal of Applied Earth Observation and Geoinformation，2023，117：103191.
[106] BLANCO T，MARTíN-SEGURA S，DE LARRINZAR J L，et al.First steps toward voice user interfaces for web-based navigation of geographic information：a spanish terms study[J].Applied Sciences，2023，13（4）：2083.
[107] LIU P，KOIVISTO S，HIIPPALA T，et al.Extracting locations from sport and exercise-related social media messages using a neural network-based bilingual toponym recognition model[J].Journal of Spatial Information Science，2022（24）：31-61.