DOCSLIB.ORG

Predicting the Correct Entities in Named-Entity Linking

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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%. Contents Acknowledgments5 1. Introduction6 1.1. Purpose and Motivation..........................6 1.2. Outline...................................7 2. Background8 2.1. Graph Theory and Concepts.......................8 2.1.1. Algorithms............................8 2.2. Knowledge Graphs............................ 10 2.2.1. Knowledge Representation.................... 10 2.2.2. Knowledge Bases......................... 10 2.3. Named-Entity Linking Components................... 11 2.3.1. Named-Entity Recognition.................... 11 2.3.2. Candidate Generation via Locality Sensitivity Hashing.... 11 2.3.3. Named-Entity Disambiguation................. 12 2.4. Feature Embeddings............................ 13 2.4.1. Word Embeddings........................ 13 2.4.2. Graph Embeddings........................ 14 2.5. Neural Networks and Deep Learning.................. 16 2.5.1. Long Short Term Memory (LSTM)............... 16 2.5.2. Convolutional Neural Networks (CNN)............. 16 2.5.3. Contextual Embeddings from Language Models (ELMo)... 17 3. Methodology 19 3.1. Named-Entity Linking.......................... 19 3.2. Disambiguation Models.......................... 20 3.2.1. Embeddings............................ 20 3.2.2. BiLSTM Model.......................... 21 3.2.3. CNN-BiLSTM Model....................... 21 3.2.4. ELMo Model........................... 21 3.2.5. Feed-Forward Neural Network (FFNN)............. 22 4. Experiments 23 4.1. Data..................................... 23 4.1.1. Kensho-Derived Wikimedia Dataset (KDWD)......... 23 4.1.2. AIDA CoNLL-YAGO Dataset (CoNLL03/AIDA)........ 24 4.2. Settings................................... 24 4.3. Evaluation Metrics............................. 26 4.3.1. Precision and Recall....................... 26 4.3.2. Classication Metrics....................... 26 4.4. Results................................... 27 4.4.1. The Eect of Training Data Size................. 27 4.4.2. Disambiguation Models..................... 27 4.4.3. Candidate List Accuracy..................... 28 4.4.4. Analysis and Discussion..................... 29 3 5. Conclusion and Future Work 31 A. Named-Entity Linking Examples 33 4 Acknowledgments I would like to thank my university supervisor Gongbo Tang for his help and guidance in the structure and pragmatics of my thesis. I am deeply indebted to the Seavus AB team, particularly my company supervisor Thorsten Jacobs, for their support, feedback, resources, ideas, and deadlines for this thesis. I would like to thank COVID-19 for destroying my social life in the months leading up to completing my thesis. Finally, I would like to thank my family, friends, and girlfriend for their ongoing support during my Master’s studies. Seavus AB is an IT consulting rm that provides enterprise-wide business solutions across the world, mainly covering the US and European markets. The department I conducted this work in was their articial intelligence and machine learning division located in Stockholm. Their current work includes but is not limited to chatbots, QA systems, and business intelligence. 5 1. Introduction "I am convinced that the crux of the problem of learning is recognizing relationships and being able to use them" Christopher Strachey in a letter to Alan Turing, 1954 Knowledge graphs have been exploding in recent years within the scope of natural language processing. Whether it be natural language generation, question-answering, or named-entity recognition and relation linking, when common natural language tasks are leveraged with knowledge graphs, improvements can be made across tasks and domains. That is why I sought to construct a named-entity linking system, whereupon ambiguities in the named-entities can be detected and properly claried with assistance from knowledge graphs. On some initial work with named-entity recognition of a corpus, I noticed "Bush" would come up several times without any clarity to whether this person was in reference to "George H.W. Bush" or "George W. Bush". For our purposes, this seemed like a glaring oversight and one that we chose to expand on to nd a proper solution. One clear method to solve this problem is through named- entity linking. A named-entity linking system consists of three primary components: named-entity recognition, candidate generation, and named-entity disambiguation. With all three of these components, we eectively identify entities, construct a list of possible candidate for the identied entities, and nally disambiguate these entities from the candidate list and link to a distinctive identier within a knowledge graph. It is this nal component – named-entity disambiguation – that I focused on for my research and evaluation. The data I originally trained the disambiguation component on was the Kensho-Derived Wikimedia Dataset, which includes Wikipedia text, links, and the Wikidata knowledge base. I then conducted further studies with the AIDA CoNLL-YAGO Dataset, a benchmark in named-entity linking. Furthermore, I performed named-entity linking with three disambiguation models that map between contextual word embeddings and knowledge graph embeddings. To optimize named-entity linking with deep learning, I treated the problem as a classication task; the models predict the correct entity among a series of candidates. With my best model performance with the benchmark dataset, I achieved 87% recall, 73% precision, and an ROC-AUC of 83%. The thesis project was conducted at Seavus AB, an IT consulting rm in Stockholm, Sweden. It was founded in Malmo, Sweden, yet has oces throughout Northern and Southeastern Europe. Seavus AB oers state-of-the-art machine learning and articial intelligence services to companies from around the world. 1.1. Purpose and Motivation The purpose of this thesis is to examine and evaluate dierent ways to improve on named-entity linking systems by mapping between context embeddings with knowledge graph embeddings for correct entities; it would appear that there is bereft research into the usage of both of these embeddings together. The motivation came from my initial ndings of ambiguity with certain persons and places, such as in the "Bush" example. Particularly when we are working with news corpora, a last 6 name can often be found alone, leading to more than a mere isolated incident. Named- entity linking is a step toward more accurate semantic representation, and synonym extraction, that remains a challenge in NLP research despite the robust ontology networks and lexicons widely available. Not only that, the relations we make in how we think, what we watch, and who we connect with are all inexorably linked together to how we have come to understand the world, and fundamental to language technology. There are several research questions I considered to help me move forward with my work. Can entities be adequately predicted through the concatenation of knowledge graph embeddings with contextual word embeddings? Most current disambiguation methods rely purely on the contextual information of documents. Can my system manage name variations – i.e., the same entity can appear with various naming conven- tions? This may be caused by aliases, spelling errors, or abbreviations. Can my system manage ambiguity – i.e., the same mention may be polysemous (i.e. have multiple meanings) depending on the specic context? Can my system manage incomplete information when there is a limited amount of knowledge? Will my system be able to ll the contextual gaps? 1.2. Outline The outline of the thesis will consist of the following. In Chapter 2, I will introduce essential graph theory concepts and terminology that will be necessary to understand the rest of the thesis. This will be followed by knowledge graphs, the components used for the named-entity linking system,
Load more

Recommended publications
  • 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]
  • Design Challenges for Entity Linking
    Design Challenges for Entity Linking Xiao Ling Sameer Singh Daniel S. Weld University of Washington, Seattle WA fxiaoling,sameer,[email protected] Abstract provide semantic annotations to human readers but also a machine-consumable representation of Recent research on entity linking (EL) has in- the most basic semantic knowledge in the text. troduced a plethora of promising techniques, Many other NLP applications can benefit from ranging from deep neural networks to joint in- ference. But despite numerous papers there such links, such as distantly-supervised relation is surprisingly little understanding of the state extraction (Craven and Kumlien, 1999; Riedel et al., of the art in EL. We attack this confusion by 2010; Hoffmann et al., 2011; Koch et al., 2014) that analyzing differences between several versions uses EL to create training data, and some coreference of the EL problem and presenting a simple systems that use EL for disambiguation (Hajishirzi yet effective, modular, unsupervised system, et al., 2013; Zheng et al., 2013; Durrett and called VINCULUM, for entity linking. We con- Klein, 2014). Unfortunately, in spite of numerous duct an extensive evaluation on nine data sets, papers on the topic and several published data comparing VINCULUM with two state-of-the- art systems, and elucidate key aspects of the sets, there is surprisingly little understanding about system that include mention extraction, candi- state-of-the-art performance. date generation, entity type prediction, entity We argue that there are three reasons
    [Show full text]