Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Data Compilation
Data Sources
Conclusion
References
All Topics
Computer Science

Biographical Semi-Supervised Relation Extraction Dataset

Profile image of Ruslan MitkovRuslan Mitkov

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

https://doi.org/10.1145/3477495.3531742
visibility

…

description

10 pages

link

1 file

Abstract

Extracting biographical information from online documents is a popular research topic among the information extraction (IE) community. Various natural language processing (NLP) techniques such as text classification, text summarisation and relation extraction are commonly used to achieve this. Among these techniques, RE is the most common since it can be directly used to build biographical knowledge graphs. RE is usually framed as a supervised machine learning (ML) problem, where ML models are trained on annotated datasets. However, there are few annotated datasets for RE since the annotation process can be costly and time-consuming. To address this, we developed Biographical, the first semi-supervised dataset for RE. The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata. By exploiting the structure of Wikipedia articles and robust named entity recognition (NER), we match information with relatively high precision in order to compile annotated relation pairs for ten different relations that are important in the DH domain. Furthermore, we demonstrate the effectiveness of the dataset by training a state-of-the-art neural model to classify relation pairs, and evaluate it on a manually annotated gold standard set. Biographical is primarily aimed at training neural models for RE within the domain of digital humanities and history, but as we discuss at the end of this paper, it can be useful for other purposes as well. CCS CONCEPTS • Computing methodologies → Information extraction; Language resources.

Related papers

Information Extraction From Wikipedia Articles Using DeepDive
Deepti Ameta

—Mining the required information from an enormous amount of data is a critical task. It is arduous to deal with the heterogeneity of astronomically immense data. Withal processing and analyzing it requires a number of resources. In the proposed work, information extraction (relation extraction between two Named entity mentions) from Wikipedia text articles using DeepDive has been carried out. We have experimented Deep-Dive's challenging capabilities for relation extraction, mention extraction and have calculated the probabilities of every variable being true via statistical learning and inference. Further we have tested results by plotting calibration plots on training data set and on whole data set by considering performance measures to check its efficiency on Wikipedia articles.

View PDFchevron_right
Large-scale data harvesting for biographical data
Constantin Orasan

2019

This paper explores automatic methods to identify relevant biography candidates in large databases, and extract biographical information from encyclopedia entries and databases. In this work, relevant candidates are defined as people who have made an impact in a certain country or region within a pre-defined time frame. We investigate the case of people who had an impact in the Republic of Austria and died between 1951 and 2019. We use Wikipedia and Wikidata as data sources and compare the performance of our information extraction methods on these two databases. We demonstrate the usefulness of a natural language processing pipeline to identify suitable biography candidates and, in a second stage, extract relevant information about them. Even though they are considered by many as an identical resource, our results show that the data from Wikipedia and Wikidata differs in some cases and they can be used in a complementary way providing more data for the compilation of biographies

View PDFchevron_right
An Unsupervised Approach to Biography Production Using Wikipedia
Elena Filatova

Meeting of the Association for Computational Linguistics, 2008

We describe an unsupervised approach to multi-document sentence-extraction based summarization for the task of producing biographies. We utilize Wikipedia to auto- matically construct a corpus of biographical sentences and TDT4 to construct a corpus of non-biographical sentences. We build a biographical-sentence classifier from these corpora and an SVM regression model for sentence ordering from the Wikipedia corpus. We evaluate our

View PDFchevron_right
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction
Wei-Yun Ma

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019

In this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations. In contrast to previous baselines, we consider the interaction between named entities and relations via a relation-weighted GCN to better extract relations. Linear and dependency structures are both used to extract both sequential and regional features of the text, and a complete word graph is further utilized to extract implicit features among all word pairs of the text. With the graph-based approach, the prediction for overlapping relations is substantially improved over previous sequential approaches. We evaluate GraphRel on two public datasets: NYT and WebNLG. Results show that GraphRel maintains high precision while increasing recall substantially. Also, GraphRel outperforms previous work by 3.2% and 5.8% (F1 score), achieving a new state-of-the-art for relation extraction.

View PDFchevron_right
Supervised Entity and Relation Extraction
Mason Smith

We present a system for extracting entities and relations from documents: given a natural text document, identify and classify entities mentioned in the document (e.g. people, locations, etc.) and relations between these entities (e.g. person X lives in location Y). We designed separate systems for relation extraction given already-labeled entities, and for entity extraction from plain text, and then combined the two systems in a pipeline. We ran our system on a small set of sports articles and two larger sets containing biomedical and newswire articles. Both entity extraction and relation extraction are trained in a supervised manner using annotations in the datasets. For entity extraction these annotations allow us to train a conditional random field sequence classifier by matching annotated types to part of speech parse trees that are built from the text. For relation extraction we ran logistic regression using a set of syntactic and surface features of the sentence data. We eval...

View PDFchevron_right
Exploiting Syntactic and Semantic Information for Relation Extraction from Wikipedia
Dat Nguyen

The exponential growth of Wikipedia recently attracts the attention of a large number of researchers and practitioners. However, one of the current challenges on Wikipedia is to make the encyclopedia processable for machines. In this paper, we deal with the problem of extracting relations between entities from Wikipedia's English articles, which can straightforwardly be transformed into Semantic Web meta data. We propose a novel method to exploit syntactic and semantic information for relation extraction. We mine frequent subsequences from the path between an entity pair in the syntactic and semantic structure in order to explore key patterns reflecting the relationship between the pair. In addition, our method can utilize the nature of Wikipedia to automatically obtain training data. The preliminary results of our experiments strongly support our hyperthesis that analyzing language in higher level is better for relation extraction on Wikipedia and show that our method is promising for text understanding.

View PDFchevron_right
An integrated approach for relation extraction from Wikipedia texts
M. Ishizuka

Linguistic-based methods and web mining-based methods are two types of leading methods for semantic relation extraction task. By integrating linguistic analysis with frequent Web information, this paper presents an unsupervised relation extraction approach, for discovering and enhancing relations in which a specified concept participates. We focus on concepts described in Wikipedia articles. By making use of the characteristics of Wikipedia and Web corpus, we define a novel distance function and develop a linear clustering algorithm on the combination of two kinds of patterns: dependency patterns from dependency analysis of texts in Wikipedia, surface patterns generated from high redundant information from the Web corpus. The experiments on two different domains demonstrate the superiority of our approach comparing with previous method. In essence, our approach shows how deep linguistic features contribute complementally with Web surface features to generate a broad variety of relations.

View PDFchevron_right
Relation Extraction from Wikipedia Using Subtree Mining
Dat Nguyen

2007

The exponential growth and reliability of Wikipedia have made it a promising data source for intelligent systems. The first challenge of Wikipedia is to make the encyclopedia machine-processable. In this study, we address the problem of extracting relations among entities from Wikipedia's English articles, which in turn can serve for intelligent systems to satisfy users' information needs. Our proposed method first anchors the appearance of entities in Wikipedia articles using some heuristic rules that are supported by their encyclopedic style. Therefore, it uses neither the Named Entity Recognizer (NER) nor the Coreference Resolution tool, which are sources of errors for relation extraction. It then classifies the relationships among entity pairs using SVM with features extracted from the web structure and subtrees mined from the syntactic structure of text. The innovations behind our work are the following: a) our method makes use of Wikipedia characteristics for entity allocation and entity classification, which are essential for relation extraction; b) our algorithm extracts a core tree, which accurately reflects a relationship between a given entity pair, and subsequently identifies key features with respect to the relationship from the core tree. We demonstrate the effectiveness of our approach through evaluation of manually annotated data from actual Wikipedia articles.

View PDFchevron_right
Creating a Dead Poets Society: Extracting a social network of historical persons from the web
Jan Korst

2007

We present a simple method to extract information from search engine snippets. Although the techniques presented are domain independent, this work focuses on extracting biographical information of historical persons from multiple unstructured sources on the Web. We first similarly find a list of persons and their periods of life by querying the periods and scanning the retrieved snippets for person names. Subsequently, we find biographical information for the persons extracted. In order to get insight in the mutual relations among the persons identified, we create a social network using co-occurrences on the Web. Although we use uncontrolled and unstructured Web sources, the information extracted is reliable. Moreover we show that Web Information Extraction can be used to create both informative and enjoyable applications.

View PDFchevron_right
Extracting Relations from Italian Wikipedia Using Self-Training
Giovanni Semeraro

Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

In this paper, we describe a supervised approach for extracting relations from Wikipedia. In particular, we exploit a self-training strategy for enriching a small number of manually labeled triples with new self-labeled examples. We integrate the supervised stage in WikiOIE, an existing framework for unsupervised extraction of relations from Wikipedia. We rely on WikiOIE and its unsupervised pipeline for extracting the initial set of unlabelled triples. An evaluation involving different algorithms and parameters proves that self-training helps to improve performance. Finally, we provide a dataset of about three million triples extracted from the Italian version of Wikipedia and perform a preliminary evaluation conducted on a sample dataset, obtaining promising results.

View PDFchevron_right

References (40)

  1. 1913. The quarterly army list for the quarter ending April 1914.
  2. Christoph Alt, Marc Hübner, and Leonhard Hennig. 2019. Improving Relation Extraction by Pre-trained Language Representations. In Automated Knowledge Base Construction (AKBC). https://openreview.net/forum?id=BJgrxbqp67
  3. Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 2019. Matching the Blanks: Distributional Similarity for Relation Learning. In Proceedings of ACL 2019. ACL, Florence, Italy, 2895-2905. https://aclanthology. org/P19-1279
  4. Fadi Biadsy, Julia Hirschberg, and Elena Filatova. 2008. An Unsupervised Ap- proach to Biography Production Using Wikipedia. In Proceedings of ACL 2008: HLT. 807-815.
  5. Andrew Chisholm, Will Radford, and Ben Hachey. 2017. Learning to generate one-sentence biographies from Wikidata. CoRR abs/1702.0 (2017). http://arxiv. org/abs/1702.06235 _eprint: 1702.06235.
  6. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL 2019: HLT. ACL, Minneapolis, Minnesota, 4171-4186. https://www.aclweb.org/anthology/N19-1423
  7. Matthew R. Gormley, Mo Yu, and Mark Dredze. 2015. Improved Relation Ex- traction with Feature-Rich Compositional Embedding Models. In Proceedings of EMNLP 2015. ACL, Lisbon, Portugal, 1774-1784. https://aclanthology.org/D15- 1205
  8. Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. 2010. SemEval-2010 Task 8: Multi-Way Classification of Seman- tic Relations between Pairs of Nominals. In Proceedings of the 5th Interna- tional Workshop on Semantic Evaluation. ACL, Uppsala, Sweden, 33-38. https: //aclanthology.org/S10-1006
  9. Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S. Weld. 2011. Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations. In Proceedings of ACL 2011: HLT. ACL, Portland, Oregon, USA, 541-550. https://aclanthology.org/P11-1055
  10. Alexander Hogue, Joel Nothman, and James R Curran. 2014. Unsupervised Biographical Event Extraction Using Wikipedia Traffic. In Proceedings of the Australasian Language Technology Association Workshop 2014. 41-49.
  11. Chen Jia, Yuefeng Shi, Qinrong Yang, and Yue Zhang. 2020. Entity Enhanced BERT Pre-training for Chinese NER. In Proceedings of EMNLP 2020. ACL, Online, 6384-6396. https://doi.org/10.18653/v1/2020.emnlp-main.518
  12. Jing Jiang. 2012. Information Extraction from Text. Springer US, Boston, MA, 11-41. https://doi.org/10.1007/978-1-4614-3223-4_2
  13. Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2020. SpanBERT: Improving Pre-training by Representing and Predicting Spans. Transactions of ACL 8 (2020), 64-77. https://aclanthology.org/ 2020.tacl-1.5
  14. Yudong Liu, Zhongmin Shi, and Anoop Sarkar. 2007. Exploiting Rich Syntactic Information for Relation Extraction from Biomedical Articles. In Proceedings of NAACL 2007: HLT (Rochester, New York) (NAACL-Short '07). Association for Computational Linguistics, USA, 97-100.
  15. Makoto Miwa and Mohit Bansal. 2016. End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. In Proceedings of ACL 2016. Association for Computational Linguistics, Berlin, Germany, 1105-1116. https://doi.org/10. 18653/v1/P16-1105
  16. Zara Nasar, Syed Waqar Jaffry, and Muhammad Kamran Malik. 2021. Named Entity Recognition and Relation Extraction: State-of-the-Art. ACM Comput. Surv. 54, 1, Article 20 (feb 2021), 39 pages. https://doi.org/10.1145/3445965
  17. Tapas Nayak and Hwee Tou Ng. 2020. Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction. Proceedings of the AAAI Conference on Artificial Intelligence 34, 05 (Apr. 2020), 8528-8535. https://ojs.aaai. org/index.php/AAAI/article/view/6374
  18. Paul Over and James Yen. 2004. An introduction to DUC-2004. National Institute of Standards and Technology (2004).
  19. Alessio Palmero Aprosio and Sara Tonelli. 2015. Recognizing Biographical Sec- tions in Wikipedia. In Proceedings of EMNLP 2015. ACL, Lisbon, Portugal, 811-816. https://aclanthology.org/D15-1095
  20. Alistair Plum, Marcos Zampieri, Constantin Orăsan, Eveline Wandl-Vogt, and Ruslan Mitkov. 2019. Large-scale Data Harvesting for Biographical Data. In Proceedings of (BD-2019).
  21. Tharindu Ranasinghe and Marcos Zampieri. 2020. Multilingual Offensive Lan- guage Identification with Cross-lingual Embeddings. In Proceedings of EMNLP 2020. ACL, Online, 5838-5844. https://doi.org/10.18653/v1/2020.emnlp-main.470
  22. Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010. Modeling Relations and Their Mentions without Labeled Text. In Machine Learning and Knowledge Discovery in Databases, José Luis Balcázar, Francesco Bonchi, Aristides Gionis, and Michèle Sebag (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 148-163.
  23. Yatian Shen and Xuanjing Huang. 2016. Attention-Based Convolutional Neu- ral Network for Semantic Relation Extraction. In Proceedings of COLING 2016: Technical Papers. Osaka, Japan, 2526-2536. https://aclanthology.org/C16-1238
  24. Ayush Singhal, Michael Simmons, and Zhiyong Lu. 2016. Text mining for preci- sion medicine: automating disease-mutation relationship extraction from biomed- ical literature. J Am Med Inform Assoc 23, 4 (April 2016), 766-772.
  25. Alisa Smirnova and Philippe Cudré-Mauroux. 2018. Relation Extraction Using Distant Supervision: A Survey. ACM Comput. Surv. 51, 5, Article 106 (nov 2018), 35 pages. https://doi.org/10.1145/3241741
  26. Joseph Turian, Lev-Arie Ratinov, and Yoshua Bengio. 2010. Word Representations: A Simple and General Method for Semi-Supervised Learning. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. ACL, Uppsala, Sweden, 384-394. https://aclanthology.org/P10-1040
  27. Denny Vrandečić and Markus Krötzsch. 2014. Wikidata: a free collaborative knowledgebase. Commun. ACM 57, 10 (2014), 78-85.
  28. Hailin Wang, Guoming Lu, Jin Yin, and Ke Qin. 2021. Relation Extraction: A Brief Survey on Deep Neural Network Based Methods. In 2021 The 4th International Conference on Software Engineering and Information Management (Yokohama, Japan) (ICSIM 2021). ACM, New York, NY, USA, 220-228. https://doi.org/10.1145/ 3451471.3451506
  29. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proceedings of EMNLP 2020: System Demonstrations. ACL, Online, 38-45. https://aclanthology.org/2020.emnlp-demos.6
  30. Shanchan Wu and Yifan He. 2019. Enriching Pre-Trained Language Model with Entity Information for Relation Classification. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (Beijing, China) (CIKM '19). Association for Computing Machinery, New York, NY, USA, 2361-2364. https://doi.org/10.1145/3357384.3358119
  31. Minguang Xiao and Cong Liu. 2016. Semantic Relation Classification via Hierar- chical Recurrent Neural Network with Attention. In Proceedings of COLING 2016: Technical Papers. Osaka, Japan, 1254-1263. https://aclanthology.org/C16-1119
  32. Fuzhao Xue, Aixin Sun, Hao Zhang, and Eng Siong Chng. 2021. GDPNet: Refining Latent Multi-View Graph for Relation Extraction. Proceedings of the AAAI Conference on Artificial Intelligence 35, 16 (May 2021), 14194-14202. https://ojs.aaai.org/index.php/AAAI/article/view/17670
  33. Kui Xue, Yangming Zhou, Zhiyuan Ma, Tong Ruan, Huanhuan Zhang, and Ping He. 2019. Fine-tuning BERT for Joint Entity and Relation Extraction in Chi- nese Medical Text. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 892-897. https://doi.org/10.1109/BIBM47256.2019.8983370
  34. Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, and Yuji Matsumoto. 2020. LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention. In Proceedings of EMNLP 2020. ACL, Online, 6442-6454. https: //doi.org/10.18653/v1/2020.emnlp-main.523
  35. Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019. End-to-End Open-Domain Question Answering with BERTserini. In Proceedings of NAACL 2019. ACL, Minneapolis, Minnesota, 72-77. https://doi.org/10.18653/v1/N19-4013
  36. Amy Zhao Yu, Shahar Ronen, Kevin Hu, Tiffany Lu, and César A Hidalgo. 2016. Pantheon 1.0, a Manually Verified Dataset of Globally Famous Biographies. Sci- entific data 3, 1 (2016), 1-16.
  37. Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. 2014. Relation Classification via Convolutional Deep Neural Network. In Proceedings of COLING 2014: Technical Papers. Dublin City University and ACL, Dublin, Ireland, 2335- 2344. https://aclanthology.org/C14-1220
  38. Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning. 2017. Position-aware Attention and Supervised Data Improve Slot Filling. In Proceedings of EMNLP 2017. ACL, Copenhagen, Denmark, 35-45. https: //aclanthology.org/D17-1004
  39. Liang Zhou, Miruna Ticrea, and Eduard Hovy. 2004. Multi-Document Biography Summarization. In Proceedings of EMNLP 2004. ACL, Barcelona, Spain, 434-441. https://aclanthology.org/W04-3256
  40. Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao, and Bo Xu. 2016. Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification. In Proceedings of ACL 2016. ACL, Berlin, Germany, 207-212. https://doi.org/10.18653/v1/P16-2034

Related papers

Biographical: A Semi-Supervised Relation Extraction Dataset
Constantin Orasan

arXiv (Cornell University), 2022

Extracting biographical information from online documents is a popular research topic among the information extraction (IE) community. Various natural language processing (NLP) techniques such as text classification, text summarisation and relation extraction are commonly used to achieve this. Among these techniques, RE is the most common since it can be directly used to build biographical knowledge graphs. RE is usually framed as a supervised machine learning (ML) problem, where ML models are trained on annotated datasets. However, there are few annotated datasets for RE since the annotation process can be costly and time-consuming. To address this, we developed Biographical, the first semi-supervised dataset for RE. The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata. By exploiting the structure of Wikipedia articles and robust named entity recognition (NER), we match information with relatively high precision in order to compile annotated relation pairs for ten different relations that are important in the DH domain. Furthermore, we demonstrate the effectiveness of the dataset by training a state-of-the-art neural model to classify relation pairs, and evaluate it on a manually annotated gold standard set. Biographical is primarily aimed at training neural models for RE within the domain of digital humanities and history, but as we discuss at the end of this paper, it can be useful for other purposes as well. CCS CONCEPTS • Computing methodologies → Information extraction; Language resources.

View PDFchevron_right
BiographyNet: Extracting Relations Between People and Events
susan legêne

ArXiv, 2018

This paper describes BiographyNet, a digital humanities project (2012-2016) that brings together researchers from history, computational linguistics and computer science. The project uses data from the Biography Portal of the Netherlands (BPN), which contains approximately 125,000 biographies from a variety of Dutch biographical dictionaries from the eighteenth century until now, describing around 76,000 individuals. BiographyNet's aim is to strengthen the value of the portal and comparable biographical datasets for historical research, by improving the search options and the presentation of its outcome, with a historically justified NLP pipeline that works through a user evaluated demonstrator. The project's main target group are professional historians. The project therefore worked with two key concepts: ``provenance'' -understood as a term allowing for both historical source criticism and for references to data-management and programming interventions in digitized...

View PDFchevron_right
Exploring the effectiveness of linguistic knowledge for biographical relation extraction
marcos garcia

Natural Language Engineering, 2013

Machine learning techniques have been implemented to extract instances of semantic relations using diverse features based on linguistic knowledge, such as tokens, lemmas, PoS-tags, or dependency paths. However, there has been little work aiming to know which of these features works better in the relation extraction task, and less in languages other than English. In this paper, various features representing different levels of linguistic knowledge are systematically evaluated for biographical relation extraction. The effectiveness of these features was measured by training several supervised classifiers that only differ in the type of linguistic knowledge used to define their features. The experiments performed in this paper show that some basic linguistic knowledge (provided by lemmas and their combination in bigrams) behaves better than other complex features, such as those based on syntactic analysis. Furthermore, some feature combinations using different levels of analysis are pr...

View PDFchevron_right
Recognizing Biographical Sections in Wikipedia
Alessio Palmero Aprosio

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015

Wikipedia is the largest collection of encyclopedic data ever written in the history of humanity. Thanks to its coverage and its availability in machine-readable format, it has become a primary resource for largescale research in historical and cultural studies. In this work, we focus on the subset of pages describing persons, and we investigate the task of recognizing biographical sections from them: given a person's page, we identify the list of sections where information about her/his life is present. We model this as a sequence classification problem, and propose a supervised setting, in which the training data are acquired automatically. Besides, we show that six simple features extracted only from the section titles are very informative and yield good results well above a strong baseline.

View PDFchevron_right
Creating Biographical Networks from Chinese and English Wikipedia
Nora van den Bosch

2021

With the rise of digital humanities, historians explore how to intellectually engage with textual sources given the available computational tools of today. The ENP-China project employs Natural Language Processing methods to tap into sources of unprecedented scale with the goal to study the transformation of elites in Modern China (1830–1949). One of the subprojects is extracting various kinds of data from biographies and, for that, we created a large corpus of biographies automatically collected from the Chinese and English Wikipedia. The dataset contains 228,144 biographical articles from the offline Chinese Wikipedia copy and is supplemented with 110,713 English biographies that are linked to a Chinese page. We also enriched this bilingual corpus with metadata that records every mentioned person, organization, geopolitical entity and location per Wikipedia biography and links the names to their counterpart in the other language. This data structure allows the researcher to analyz...

View PDFchevron_right

Related topics

  • Computer Science
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved