DOCSLIB.ORG

Entity Linking Based on Sentence Representation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Entity Linking Based on Sentence Representation Hindawi Complexity Volume 2021, Article ID 8895742, 9 pages https://doi.org/10.1155/2021/8895742 Research Article Entity Linking Based on Sentence Representation Bingjing Jia ,1,2 Zhongli Wu,2 Pengpeng Zhou ,1 and Bin Wu 1 1Beijing University of Posts and Telecommunications, Beijing 100876, China 2Anhui Science and Technology University, Bengbu 233000, China Correspondence should be addressed to Bin Wu; [email protected] Received 12 September 2020; Revised 26 October 2020; Accepted 8 January 2021; Published 19 January 2021 Academic Editor: Wei Wang Copyright © 2021 Bingjing Jia et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most existing methods failed to link when a mention appears multiple times in a document, since the conflict of its contexts in different locations may lead to difficult linking. Sentence representation, which has been studied based on deep learning approaches recently, can be used to resolve the above issue. In this paper, an effective entity linking model is proposed to capture the semantic meaning of the sentences and reduce the noise introduced by different contexts of the same mention in a document. )is model first uses the symmetry of the Siamese network to learn the sentence similarity. )en, the attention mechanism is added to improve the interaction between input sentences. To show the effectiveness of our sentence representation model combined with attention mechanism, named ELSR, extensive experiments are conducted on two public datasets. Results illustrate that our model outperforms the baselines and achieves the superior performance. 1. Introduction commonly rank entities based on a measure of similarity between the mention and the entity to determine the best With the development of big data, a large number of un- candidate. Various types of features have been designed, structured texts have appeared on the Internet, and almost including entity popularity, entity type, and entity co-oc- all of the mentions in the texts are ambiguous. For example, currence. For entity popularity, Milne and Witten [5] Figure 1 gives a snippet “Spain and U.S. teams are very achieved 90% accuracy on the Wikipedia test articles using competitive. )is year’s Fed Cup finalists—defending the prior probability. For entity type, Nie et al. [6] used type champion Spain and the United States—will hit the road to information to improve the co-attention effect. Chen et al. open the 1997 women’s international team competition,” in and Gupta et al. [7, 8] captured latent entity type infor- which the mention “Spain” may refer to Spain, the Spain mation through pretrained entity embedding. For entity co- national football team, the Spanish Empire, or the Spain Fed occurrence, graph-based methods are explored to improve Cup team. When the context of the mention is different, it the impact of coherence. Guo and Barbosa [9] obtained the may refer to different entities. )ere are thousands of entities indirect connections of entities through random walks on in a knowledge base (KB), which are the basis of many the disambiguation graph. )ere is some useful information research studies [1]. )erefore, KB is regarded as surrogates in these statistics and features, which makes the entity for real-world entities. )e main purpose of entity linking linking problem easier. In most cases, these statistics and focuses on linking mentions in text to corresponding entities features are provided by KB. )e separate entity records only in a KB such as Freebase. Entity linking is beneficial to fully have description information when the incomplete KB is understanding these texts [2–4], and it also can boost the sparse and simple [10]. )erefore, the above methods may development of question answering, machine reading, and not perform well. It will be expensive and time consuming to knowledge base population. inject structural features and statistics into entities manually. )e task of entity linking is challenging because of in- So, only the entity description, which is the most common herent ambiguity of natural language. Previous studies information in KB, is considered in this paper. In addition, 2 Complexity Spain : officially the Kingdom of Spain, is a sovereign state located on the Iberian Peninsula in southwestern Europe. Spain national football team : represents Spain in International association football and Spain and U.S. teams are very competitive. This year’s is controlled by the Royal Spanish Football Federation, the Fed Cup finalists—defending champion Spain and governing body for football in Spain. the United States—will hit the road to open the 1997 Spanish Empire: women’s international team competition. was one of the largest empires in world history and one of the first of global extent. Spain Fed Cup team : represents Spain in Fed Cup tennis competition and are governed by the Real Federación Española de Tenis. Figure 1: Illustration of mentions in the snippet and their candidate entities in the knowledge base. Solid lines point to the correct target entities corresponding to the mentions and to the descriptions of these correct target entities. when a mention appears multiple times in a document, the work attempted to develop sophisticated features to rank the conflict of its contexts in different locations may lead to entities [11]. )e results showed that the context features and difficult linking. As shown in Figure 1, the first and second the special features perform well. )en, the hidden infor- occurrences of mention “Spain” may link to an entity of mation generated by the entity-topic model was used to “Spain” and an entity of “Spain Fed Cup team,” respectively. enhance the context feature [12]. Besides, link information It is necessary to take information from a sentence where a in KB was leveraged to construct the graph, which was mention appears. combined with four confidence scores [13]. Lexical and )erefore, ELSR is proposed at a sentence level. )is statistical features were added into the unified semantic model uses the symmetry of Siamese network to derive the representation for documents and entities to solve the all- dependencies between the mention context and entity de- against-all matching problem [9]. scription, which also alleviates the issue caused by the in- To reduce time and alleviate manual work, recent neural complete knowledge base. )e main contributions of this network-based models have been investigated to capture paper are listed as follows: semantic features of mentions and entities, which achieved the state-of-the-art performance. In [14], a denoising (i) A novel symmetrical neural model is proposed for autoencoder was first applied to encode documents and entity linking, which not only captures the semantic entities. Francislandau et al. [15] combined sparse feature meaning of the sentences but also reduces the noise with multiple granularity information learned by convolu- introduced by different contexts of the same tional neural networks (CNNs). Sun et al. [16] utilized mention in a document. memory network to automatically find key information for a (ii) )e key information extracted only from the mention from surrounding contexts to facilitate entity mention context and entity description are suitable linking. Ganea and Hofmann [17] combined entity em- for the incomplete knowledge base, which can make beddings with the contextual attention for local linking. up the sparseness and the simplicity. Besides, the )en, the loopy belief propagation was used for global in- attention mechanism can improve the interactive ference and achieved competitive results. RLEL [18] effects between two sentences. regarded entity linking as a sequence decision problem and (iii) To validate the performance of the model, experi- gave the result based on a reinforcement learning model. ments are conducted over two corpora. )e results Unfortunately, their model only used the previously referred illustrate that our model is powerful in most cases. entities and failed to find the consistency of subsequent entities. Because of the flexibility and efficiency of the graph )e structure of the rest of this paper is organized as neural networks, they can capture better graph represen- follows. Section 2 presents related work. )e entity linking tations and pay attention to find the relatedness between model is detailed in Section 3, and experimental results are entities. Cao et al. [19] utilized graph convolutional network described in Section 4. Section 5 gives conclusions and to encode entity graphs. However, this model only focused suggestions for future work. on target entities, which generated lots of noisy data. Wu 2. Related Work et al. [20] added some local and global features into an entity dependency graph and utilized the graph convolutional Entity linking is a well-studied task in KB population. It is networks to capture the structural information among en- also a key step for the question answering, content analysis, tities. Fang et al. [21] generated high recall candidate sets and and information retrieval. )e early works applied extensive introduced a sequential graph attention network to obtain hand-designed features and require intensive manual efforts, the topical coherence of mentions. However, the con- including prior popularity, name string similarity, Wiki- struction of graphs is very time consuming, and the com- pedia’s category information, and so on. A representative plexity of these models is relatively high. Complexity 3 Following the burgeoning popularity of embedding advantages, our proposed model consists of four main methods on knowledge graphs, some studies tried to apply components, including embedding, fine-tuning BERT, in- embedding algorithms on entity linking. Yamada et al. [22] teraction, and prediction. )e overall architecture of the easily measured the similarities between mentions and en- model is illustrated in Figure 2.
Load more

Recommended publications
  • Predicting the Correct Entities in Named-Entity Linking
    Separating the Signal from the Noise: Predicting the Correct Entities in Named-Entity Linking Drew Perkins Uppsala University Department of Linguistics and Philology Master Programme in Language Technology Master’s Thesis in Language Technology, 30 ects credits June 9, 2020 Supervisors: Gongbo Tang, Uppsala University Thorsten Jacobs, Seavus Abstract In this study, I constructed a named-entity linking system that maps between contextual word embeddings and knowledge graph embeddings to predict correct entities. To establish a named-entity linking system, I rst applied named-entity recognition to identify the entities of interest. I then performed candidate gener- ation via locality sensitivity hashing (LSH), where a candidate group of potential entities were created for each identied entity. Afterwards, my named-entity dis- ambiguation component was performed to select the most probable candidate. By concatenating contextual word embeddings and knowledge graph embeddings in my disambiguation component, I present a novel approach to named-entity link- ing. I conducted the experiments with the Kensho-Derived Wikimedia Dataset and the AIDA CoNLL-YAGO Dataset; the former dataset was used for deployment and the later is a benchmark dataset for entity linking tasks. Three deep learning models were evaluated on the named-entity disambiguation component with dierent context embeddings. The evaluation was treated as a classication task, where I trained my models to select the correct entity from a list of candidates. By optimizing the named-entity linking through this methodology, this entire system can be used in recommendation engines with high F1 of 86% using the former dataset. With the benchmark dataset, the proposed method is able to achieve F1 of 79%.
    [Show full text]
  • Three Birds (In the LLOD Cloud) with One Stone: Babelnet, Babelfy and the Wikipedia Bitaxonomy
    Three Birds (in the LLOD Cloud) with One Stone: BabelNet, Babelfy and the Wikipedia Bitaxonomy Tiziano Flati and Roberto Navigli Dipartimento di Informatica Sapienza Universit`adi Roma Abstract. In this paper we present the current status of linguistic re- sources published as linked data and linguistic services in the LLOD cloud in our research group, namely BabelNet, Babelfy and the Wikipedia Bitaxonomy. We describe them in terms of their salient aspects and ob- jectives and discuss the benefits that each of these potentially brings to the world of LLOD NLP-aware services. We also present public Web- based services which enable querying, exploring and exporting data into RDF format. 1 Introduction Recent years have witnessed an upsurge in the amount of semantic information published on the Web. Indeed, the Web of Data has been increasing steadily in both volume and variety, transforming the Web into a global database in which resources are linked across sites. It is becoming increasingly critical that existing lexical resources be published as Linked Open Data (LOD), so as to foster integration, interoperability and reuse on the Semantic Web [5]. Thus, lexical resources provided in RDF format can contribute to the creation of the so-called Linguistic Linked Open Data (LLOD), a vision fostered by the Open Linguistic Working Group (OWLG), in which part of the Linked Open Data cloud is made up of interlinked linguistic resources [2]. The multilinguality aspect is key to this vision, in that it enables Natural Language Processing tasks which are not only cross-lingual, but also independent both of the language of the user input and of the linked data exploited to perform the task.
    [Show full text]
  • Cross Lingual Entity Linking with Bilingual Topic Model
    Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Cross Lingual Entity Linking with Bilingual Topic Model Tao Zhang, Kang Liu and Jun Zhao Institute of Automation, Chinese Academy of Sciences HaiDian District, Beijing, China {tzhang, kliu, jzhao}@nlpr.ia.ac.cn Abstract billion web pages on Internet and 31% of them are written in other languages. This report tells us that the percentage of Cross lingual entity linking means linking an entity web pages written in English are decreasing, which indicates mention in a background source document in one that English pages are becoming less dominant compared language with the corresponding real world entity in with before. Therefore, it becomes more and more important a knowledge base written in the other language. The to maintain the growing knowledge base by discovering and key problem is to measure the similarity score extracting information from all web contents written in between the context of the entity mention and the different languages. Also, inserting new extracted knowledge document of the candidate entity. This paper derived from the information extraction system into a presents a general framework for doing cross knowledge base inevitably needs a system to map the entity lingual entity linking by leveraging a large scale and mentions in one language to their entries in a knowledge base bilingual knowledge base, Wikipedia. We introduce which may be another language. This task is known as a bilingual topic model that mining bilingual topic cross-lingual entity linking. from this knowledge base with the assumption that Intuitively, to resolve the cross-lingual entity linking the same Wikipedia concept documents of two problem, the key is how to define the similarity score different languages share the same semantic topic between the context of entity mention and the document of distribution.
    [Show full text]
  • Babelfying the Multilingual Web: State-Of-The-Art Disambiguation and Entity Linking of Web Pages in 50 Languages
    Babelfying the Multilingual Web: state-of-the-art Disambiguation and Entity Linking of Web pages in 50 languages Roberto Navigli [email protected] http://lcl.uniroma1.it! ERC StG: MultilingualERC Joint Word Starting Sense Disambiguation Grant (MultiJEDI) MultiJEDI No. 259234 1! Roberto Navigli! LIDER EU Project No. 610782! Joint work with Andrea Moro Alessandro Raganato A. Moro, A. Raganato, R. Navigli. Entity Linking meets Word Sense Disambiguation: a Unified Approach. Transactions of the Association for Computational Linguistics (TACL), 2, 2014. Babelfying the Multilingual Web: state-of-the-art Disambiguation and Entity Linking Andrea Moro and Roberto Navigli 2014.05.08 BabelNet & friends3 Roberto Navigli 2014.05.08 BabelNet & friends4 Roberto Navigli Motivation • Domain-specific Web content is available in many languages • Information should be extracted and processed independently of the source/target language • This could be done automatically by means of high- performance multilingual text understanding Babelfying the Multilingual Web: state-of-the-art Disambiguation and Entity Linking Andrea Moro, Alessandro Raganato and Roberto Navigli BabelNet (http://babelnet.org) A wide-coverage multilingual semantic network and encyclopedic dictionary in 50 languages! Named Entities and specialized concepts Concepts from WordNet from Wikipedia Concepts integrated from both resources Babelfying the Multilingual Web: state-of-the-art Disambiguation and Entity Linking Andrea Moro, Alessandro Raganato and Roberto Navigli BabelNet (http://babelnet.org)
    [Show full text]
  • Named Entity Extraction for Knowledge Graphs: a Literature Overview
    Received December 12, 2019, accepted January 7, 2020, date of publication February 14, 2020, date of current version February 25, 2020. Digital Object Identifier 10.1109/ACCESS.2020.2973928 Named Entity Extraction for Knowledge Graphs: A Literature Overview TAREQ AL-MOSLMI , MARC GALLOFRÉ OCAÑA , ANDREAS L. OPDAHL, AND CSABA VERES Department of Information Science and Media Studies, University of Bergen, 5007 Bergen, Norway Corresponding author: Tareq Al-Moslmi ([email protected]) This work was supported by the News Angler Project through the Norwegian Research Council under Project 275872. ABSTRACT An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other's context.
    [Show full text]
  • Entity Linking
    Entity Linking Kjetil Møkkelgjerd Master of Science in Computer Science Submission date: June 2017 Supervisor: Herindrasana Ramampiaro, IDI Norwegian University of Science and Technology Department of Computer Science Abstract Entity linking may be of help to quickly supplement a reader with further information about entities within a text by providing links to a knowledge base containing more information about each entity, and may thus potentially enhance the reading experience. In this thesis, we look at existing solutions, and implement our own deterministic entity linking system based on our research, using our own approach to the problem. Our system extracts all entities within the input text, and then disam- biguates each entity considering the context. The extraction step is han- dled by an external framework, while the disambiguation step focuses on entity-similarities, where similarity is defined by how similar the entities’ categories are, which we measure by using data from a structured knowledge base called DBpedia. We base this approach on the assumption that simi- lar entities usually occur close to each other in written text, thus we select entities that appears to be similar to other nearby entities. Experiments show that our implementation is not as effective as some of the existing systems we use for reference, and that our approach has some weaknesses which should be addressed. For example, DBpedia is not an as consistent knowledge base as we would like, and the entity extraction framework often fail to reproduce the same entity set as the dataset we use for evaluation. However, our solution show promising results in many cases.
    [Show full text]
  • Named-Entity Recognition and Linking with a Wikipedia-Based Knowledge Base Bachelor Thesis Presentation Niklas Baumert [[email protected]]
    Named-Entity Recognition and Linking with a Wikipedia-based Knowledge Base Bachelor Thesis Presentation Niklas Baumert [[email protected]] 2018-11-08 Contents 1 Why and What?—Motivation. 1.1 What is named-entity recognition? 1.2 What is entity linking? 2 How?—Implementation. 2.1 Wikipedia-backed Knowledge Base. 2.2 Entity Recognizer & Linker. 3 Performance Evaluation. 4 Conclusion. 2 1 Why and What—Motivation. 3 Built to provide input files for QLever. ● Specialized use-case. ○ General application as an extra feature. ● Providing a wordsfile and a docsfile as output. ● Wordsfile is input for QLever. ● Support for Wikipedia page titles, Freebase IDs and Wikidata IDs. ○ Convert between those 3 freely afterwards. 4 The docsfile is a list of all sentences with IDs. record id text 1 Anarchism is a political philosophy that advocates [...] 2 These are often described as [...] 5 The wordsfile contains each word of each sentence and determines entities and their likeliness. word Entity? recordID score Anarchism 0 1 1 <Anarchism> 1 1 0.9727 is 0 1 1 a 0 1 1 political 0 1 1 philosophy 0 1 1 <Philosophy> 1 1 0.955 6 Named-entity recognition (NER) identifies nouns in a given text. ● Finding and categorizing entities in a given text. [1][2] ● (Proper) nouns refer to entities. ● Problem: Words are ambiguous. [1] ○ Same word, different entities within a category—”John F. Kennedy”, the president or his son? ○ Same word, different entities in different categories—”JFK”, person or airport? 7 Entity linking (EL) links entities to a database. ● Resolve ambiguity by associating mentions to entities in a knowledge base.
    [Show full text]
  • KORE 50^DYWC: an Evaluation Data Set for Entity Linking Based On
    Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 2389–2395 Marseille, 11–16 May 2020 c European Language Resources Association (ELRA), licensed under CC-BY-NC KORE 50DYWC: An Evaluation Data Set for Entity Linking Based on DBpedia, YAGO, Wikidata, and Crunchbase Kristian Noullet, Rico Mix, Michael Farber¨ Karlsruhe Institute of Technology (KIT) [email protected], [email protected], [email protected] Abstract A major domain of research in natural language processing is named entity recognition and disambiguation (NERD). One of the main ways of attempting to achieve this goal is through use of Semantic Web technologies and its structured data formats. Due to the nature of structured data, information can be extracted more easily, therewith allowing for the creation of knowledge graphs. In order to properly evaluate a NERD system, gold standard data sets are required. A plethora of different evaluation data sets exists, mostly relying on either Wikipedia or DBpedia. Therefore, we have extended a widely-used gold standard data set, KORE 50, to not only accommodate NERD tasks for DBpedia, but also for YAGO, Wikidata and Crunchbase. As such, our data set, KORE 50DYWC, allows for a broader spectrum of evaluation. Among others, the knowledge graph agnosticity of NERD systems may be evaluated which, to the best of our knowledge, was not possible until now for this number of knowledge graphs. Keywords: Data Sets, Entity Linking, Knowledge Graph, Text Annotation, NLP Interchange Format 1. Introduction Therefore, annotation methods can only be evaluated Automatic extraction of semantic information from texts is on a small part of the KG concerning a specific do- an open problem in natural language processing (NLP).
    [Show full text]
  • Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions Wei Shen, Jianyong Wang, Senior Member, IEEE, and Jiawei Han, Fellow, IEEE
    1 Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions Wei Shen, Jianyong Wang, Senior Member, IEEE, and Jiawei Han, Fellow, IEEE Abstract—The large number of potential applications from bridging Web data with knowledge bases have led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledge base. Potential applications include information extraction, information retrieval, and knowledge base population. However, this task is challenging due to name variations and entity ambiguity. In this survey, we present a thorough overview and analysis of the main approaches to entity linking, and discuss various applications, the evaluation of entity linking systems, and future directions. Index Terms—Entity linking, entity disambiguation, knowledge base F 1 INTRODUCTION Entity linking can facilitate many different tasks such as knowledge base population, question an- 1.1 Motivation swering, and information integration. As the world HE amount of Web data has increased exponen- evolves, new facts are generated and digitally ex- T tially and the Web has become one of the largest pressed on the Web. Therefore, enriching existing data repositories in the world in recent years. Plenty knowledge bases using new facts becomes increas- of data on the Web is in the form of natural language. ingly important. However, inserting newly extracted However, natural language is highly ambiguous, es- knowledge derived from the information extraction pecially with respect to the frequent occurrences of system into an existing knowledge base inevitably named entities. A named entity may have multiple needs a system to map an entity mention associated names and a name could denote several different with the extracted knowledge to the corresponding named entities.
    [Show full text]
  • Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks
    Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks Matthew Francis-Landau, Greg Durrett and Dan Klein Computer Science Division University of California, Berkeley mfl,gdurrett,klein @cs.berkeley.edu { } Abstract al., 2011), but such heuristics are hard to calibrate and they capture structure in a coarser way than A key challenge in entity linking is making ef- learning-based methods. fective use of contextual information to dis- In this work, we model semantic similarity be- ambiguate mentions that might refer to differ- tween a mention’s source document context and its ent entities in different contexts. We present a model that uses convolutional neural net- potential entity targets using convolutional neural works to capture semantic correspondence be- networks (CNNs). CNNs have been shown to be ef- tween a mention’s context and a proposed tar- fective for sentence classification tasks (Kalchbren- get entity. These convolutional networks oper- ner et al., 2014; Kim, 2014; Iyyer et al., 2015) and ate at multiple granularities to exploit various for capturing similarity in models for entity linking kinds of topic information, and their rich pa- (Sun et al., 2015) and other related tasks (Dong et rameterization gives them the capacity to learn al., 2015; Shen et al., 2014), so we expect them to be which n-grams characterize different topics. effective at isolating the relevant topic semantics for We combine these networks with a sparse lin- ear model to achieve state-of-the-art perfor- entity linking. We show that convolutions over mul- mance on multiple entity linking datasets, out- tiple granularities of the input document are useful performing the prior systems of Durrett and for providing different notions of semantic context.
    [Show full text]
  • Satellitener: an Effective Named Entity Recognition Model for the Satellite Domain
    SatelliteNER: An Effective Named Entity Recognition Model for the Satellite Domain Omid Jafari1 a, Parth Nagarkar1 b, Bhagwan Thatte2 and Carl Ingram3 1Computer Science Department, New Mexico State University, Las Cruces, NM, U.S.A. 2Protos Software, Tempe, AZ, U.S.A. 3Vigilant Technologies, Tempe, AZ, U.S.A. Keywords: Natural Language Processing, Named Entity Recognition, Spacy. Abstract: Named Entity Recognition (NER) is an important task that detects special type of entities in a given text. Existing NER techniques are optimized to find commonly used entities such as person or organization names. They are not specifically designed to find custom entities. In this paper, we present an end-to-end framework, called SatelliteNER, that its objective is to specifically find entities in the Satellite domain. The workflow of our proposed framework can be further generalized to different domains. The design of SatelliteNER in- cludes effective modules for preprocessing, auto-labeling and collection of training data. We present a detailed analysis and show that the performance of SatelliteNER is superior to the state-of-the-art NER techniques for detecting entities in the Satellite domain. 1 INTRODUCTION with having NER tools for different human languages (e.g., English, French, Chinese, etc.), domain-specific Nowadays, large amounts of data is generated daily. NER software and tools have been created with the Textual data is generated by news articles, social me- consideration that all sciences and applications have a dia such as Twitter, Wikipedia, etc. Managing these growing need for NLP. For instance, NER is used in large data and extracting useful information from the medical field (Abacha and Zweigenbaum, 2011), them is an important task that can be achieved using for the legal documents (Dozier et al., 2010), for Natural Language Processing (NLP).
    [Show full text]
  • BENNERD: a Neural Named Entity Linking System for COVID-19
    BENNERD: A Neural Named Entity Linking System for COVID-19 Mohammad Golam Sohraby,∗, Khoa N. A. Duongy,∗, Makoto Miway, z , Goran Topic´y, Masami Ikeday, and Hiroya Takamuray yArtificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST), Japan zToyota Technological Institute, Japan fsohrab.mohammad, khoa.duong, [email protected], fikeda-masami, [email protected], [email protected] Abstract in text mining system, Xuan et al.(2020b) created CORD-NER dataset with comprehensive NE an- We present a biomedical entity linking (EL) system BENNERD that detects named enti- notations. The annotations are based on distant ties in text and links them to the unified or weak supervision. The dataset includes 29,500 medical language system (UMLS) knowledge documents from the CORD-19 corpus. The CORD- base (KB) entries to facilitate the corona virus NER dataset gives a shed on NER, but they do not disease 2019 (COVID-19) research. BEN- address linking task which is important to address NERD mainly covers biomedical domain, es- COVID-19 research. For example, in the example pecially new entity types (e.g., coronavirus, vi- sentence in Figure1, the mention SARS-CoV-2 ral proteins, immune responses) by address- ing CORD-NER dataset. It includes several needs to be disambiguated. Since the term SARS- NLP tools to process biomedical texts includ- CoV-2 in this sentence refers to a virus, it should ing tokenization, flat and nested entity recog- be linked to an entry of a virus in the knowledge nition, and candidate generation and rank- base, not to an entry of ‘SARS-CoV-2 vaccination’, ing for EL that have been pre-trained using which corresponds to therapeutic or preventive pro- the CORD-NER corpus.
    [Show full text]