Named Entity Extraction for Knowledge Graphs: a Literature Overview
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
15 Things You Should Know About Spacy
15 THINGS YOU SHOULD KNOW ABOUT SPACY ALEXANDER CS HENDORF @ EUROPYTHON 2020 0 -preface- Natural Language Processing Natural Language Processing (NLP): A Unstructured Data Avalanche - Est. 20% of all data - Structured Data - Data in databases, format-xyz Different Kinds of Data - Est. 80% of all data, requires extensive pre-processing and different methods for analysis - Unstructured Data - Verbal, text, sign- communication - Pictures, movies, x-rays - Pink noise, singularities Some Applications of NLP - Dialogue systems (Chatbots) - Machine Translation - Sentiment Analysis - Speech-to-text and vice versa - Spelling / grammar checking - Text completion Many Smart Things in Text Data! Rule-Based Exploratory / Statistical NLP Use Cases � Alexander C. S. Hendorf [email protected] -Partner & Principal Consultant Data Science & AI @hendorf Consulting and building AI & Data Science for enterprises. -Python Software Foundation Fellow, Python Softwareverband chair, Emeritus EuroPython organizer, Currently Program Chair of EuroSciPy, PyConDE & PyData Berlin, PyData community organizer -Speaker Europe & USA MongoDB World New York / San José, PyCons, CEBIT Developer World, BI Forum, IT-Tage FFM, PyData London, Berlin, PyParis,… here! NLP Alchemy Toolset 1 - What is spaCy? - Stable Open-source library for NLP: - Supports over 55+ languages - Comes with many pretrained language models - Designed for production usage - Advantages: - Fast and efficient - Relatively intuitive - Simple deep learning implementation - Out-of-the-box support for: - Named entity recognition - Part-of-speech (POS) tagging - Labelled dependency parsing - … 2 – Building Blocks of spaCy 2 – Building Blocks I. - Tokenization Segmenting text into words, punctuations marks - POS (Part-of-speech tagging) cat -> noun, scratched -> verb - Lemmatization cats -> cat, scratched -> scratch - Sentence Boundary Detection Hello, Mrs. Poppins! - NER (Named Entity Recognition) Marry Poppins -> person, Apple -> company - Serialization Saving NLP documents 2 – Building Blocks II. -
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%. -
Design and Implementation of an Open Source Greek Pos Tagger and Entity Recognizer Using Spacy
DESIGN AND IMPLEMENTATION OF AN OPEN SOURCE GREEK POS TAGGER AND ENTITY RECOGNIZER USING SPACY A PREPRINT Eleni Partalidou1, Eleftherios Spyromitros-Xioufis1, Stavros Doropoulos1, Stavros Vologiannidis2, and Konstantinos I. Diamantaras3 1DataScouting, 30 Vakchou Street, Thessaloniki, 54629, Greece 2Dpt of Computer, Informatics and Telecommunications Engineering, International Hellenic University, Terma Magnisias, Serres, 62124, Greece 3Dpt of Informatics and Electronic Engineering, International Hellenic University, Sindos, Thessaloniki, 57400, Greece ABSTRACT This paper proposes a machine learning approach to part-of-speech tagging and named entity recog- nition for Greek, focusing on the extraction of morphological features and classification of tokens into a small set of classes for named entities. The architecture model that was used is introduced. The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models. Additionally, a part of speech tagger was trained that can detect the morphologyof the tokens and performs higher than the state-of-the-art results when classifying only the part of speech. For named entity recognition using spaCy, a model that extends the standard ENAMEX type (organization, location, person) was built. Certain experi- ments that were conducted indicate the need for flexibility in out-of-vocabulary words and there is an effort for resolving this issue. Finally, the evaluation results are discussed. Keywords Part-of-Speech Tagging, Named Entity Recognition, spaCy, Greek text. 1. Introduction In the research field of Natural Language Processing (NLP) there are several tasks that contribute to understanding arXiv:1912.10162v1 [cs.CL] 5 Dec 2019 natural text. -
Translation, Sentiment and Voices: a Computational Model to Translate and Analyze Voices from Real-Time Video Calling
Translation, Sentiment and Voices: A Computational Model to Translate and Analyze Voices from Real-Time Video Calling Aneek Barman Roy School of Computer Science and Statistics Department of Computer Science Thesis submitted for the degree of Master in Science Trinity College Dublin 2019 List of Tables 1 Table 2.1: The table presents a count of studies 22 2 Table 2.2: The table present a count of studies 23 3 Table 3.1: Europarl Training Corpus 31 4 Table 3.2: VidALL Neural Machine Translation Corpus 32 5 Table 3.3: Language Translation Model Specifications 33 6 Table 4.1: Speech Analysis Corpus Statistics 42 7 Table 4.2: Sentences Given to Speakers 43 8 Table 4.3: Hacki’s Vocal Intensity Comparators (Hacki, 1996) 45 9 Table 4.4: Model Vocal Intensity Comparators 45 10 Table 4.5: Sound Intensities for Sentence 1 and Sentence 2 46 11 Table 4.6: Classification of Sentiment Scores 47 12 Table 4.7: Sentiment Scores for Five Sentences 47 13 Table 4.8: Simple RNN-based Model Summary 52 14 Table 4.9: Simple RNN-based Model Results 52 15 Table 4.10: Embedded-based RNN Model Summary 53 16 Table 4.11: Embedded-RNN based Model Results 54 17 Table 5.1: Sentence A Evaluation of RNN and Embedded-RNN Predictions from 20 Epochs 63 18 Table 5.2: Sentence B Evaluation of RNN and Embedded-RNN Predictions from 20 Epochs 63 19 Table 5.3: Evaluation on VidALL Speech Transcriptions 64 v List of Figures 1 Figure 2.1: Google Neural Machine Translation Architecture (Wu et al.,2016) 10 2 Figure 2.2: Side-by-side Evaluation of Google Neural Translation Model (Wu -
A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications
The Thirty-Second Innovative Applications of Artificial Intelligence Conference (IAAI-20) A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications Shivashankar Subramanian,1,2 Ioana Baldini,1 Sushma Ravichandran,1 Dmitriy A. Katz-Rogozhnikov,1 Karthikeyan Natesan Ramamurthy,1 Prasanna Sattigeri,1 Kush R. Varshney,1 Annmarie Wang,3,4 Pradeep Mangalath,3,5 Laura B. Kleiman3 1IBM Research, Yorktown Heights, NY, USA, 2University of Melbourne, VIC, Australia, 3Cures Within Reach for Cancer, Cambridge, MA, USA, 4Massachusetts Institute of Technology, Cambridge, MA, USA, 5Harvard Medical School, Boston, MA, USA Abstract (Pantziarka et al. 2017; Bouche, Pantziarka, and Meheus 2017; Verbaanderd et al. 2017). However, manual review to More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown identify and analyze potential evidence is time-consuming promise for treating cancer. Due to their long history of safe and intractable to scale, as PubMed indexes millions of ar- patient use, low cost, and widespread availability, repurpos- ticles and the collection is continuously updated. For exam- ing of these drugs represents a major opportunity to rapidly ple, the PubMed query Neoplasms [mh] yields more than improve outcomes for cancer patients and reduce healthcare 3.2 million articles.1 Hence there is a need for a (semi-) au- costs. In many cases, there is already evidence of efficacy for tomatic approach to identify relevant scientific articles and cancer, but trying to manually extract such evidence from the provide an easily consumable summary of the evidence. scientific literature is intractable. -
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. -
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. -
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) -
Linked Data Triples Enhance Document Relevance Classification
applied sciences Article Linked Data Triples Enhance Document Relevance Classification Dinesh Nagumothu * , Peter W. Eklund , Bahadorreza Ofoghi and Mohamed Reda Bouadjenek School of Information Technology, Deakin University, Geelong, VIC 3220, Australia; [email protected] (P.W.E.); [email protected] (B.O.); [email protected] (M.R.B.) * Correspondence: [email protected] Abstract: Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is “about”, rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge-base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized news- feed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments Citation: Nagumothu, D.; Eklund, indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% P.W.; Ofoghi, B.; Bouadjenek, M.R. -
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. -
KOI at Semeval-2018 Task 5: Building Knowledge Graph of Incidents
KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents 1 2 2 Paramita Mirza, Fariz Darari, ∗ Rahmad Mahendra ∗ 1 Max Planck Institute for Informatics, Germany 2 Faculty of Computer Science, Universitas Indonesia, Indonesia paramita @mpi-inf.mpg.de fariz,rahmad.mahendra{ } @cs.ui.ac.id { } Abstract Subtask S3 In order to answer questions of type (ii), participating systems are also required We present KOI (Knowledge of Incidents), a to identify participant roles in each identified an- system that given news articles as input, builds swer incident (e.g., victim, subject-suspect), and a knowledge graph (KOI-KG) of incidental use such information along with victim-related nu- events. KOI-KG can then be used to effi- merals (“three people were killed”) mentioned in ciently answer questions such as “How many the corresponding answer documents, i.e., docu- killing incidents happened in 2017 that involve ments that report on the answer incident, to deter- Sean?” The required steps in building the KG include: (i) document preprocessing involv- mine the total number of victims. ing word sense disambiguation, named-entity Datasets The organizers released two datasets: recognition, temporal expression recognition and normalization, and semantic role labeling; (i) test data, stemming from three domains of (ii) incidental event extraction and coreference gun violence, fire disasters and business, and (ii) resolution via document clustering; and (iii) trial data, covering only the gun violence domain. KG construction and population. Each dataset -
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.