DOCSLIB.ORG

Prerequisites for Extracting Entity Relations from Swedish Texts

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

EXAMENSARBETE INOM TEKNIK, GRUNDNIVÅ, 15 HP STOCKHOLM, SVERIGE 2020 Prerequisites for Extracting Entity Relations from Swedish Texts ERIK LENAS KTH SKOLAN FÖR ELEKTROTEKNIK OCH DATAVETENSKAP Authors Erik Lenas University KTH Royal Institute of Technology Supervisor Anders Sjögren Examinor Fadil Galjic Bachelor Thesis Degree Project in Computer Engineering, First Cycle Faculty Electrical Engineering and Computer Science University KTH Royal Institute of Technology Partner Riksarkivet 2 Abstract Natural language processing (NLP) is a vibrant area of research with many practical applications today like sentiment analyses, text labeling, questioning an- swering, machine translation and automatic text summarizing. At the moment, research is mainly focused on the English language, although many other lan- guages are trying to catch up. This work focuses on an area within NLP called information extraction, and more specifically on relation extraction, that is, to ex- tract relations between entities in a text. What this work aims at is to use machine learning techniques to build a Swedish language processing pipeline with part-of- speech tagging, dependency parsing, named entity recognition and coreference resolution to use as a base for later relation extraction from archival texts. The obvious difficulty lies in the scarcity of Swedish annotated datasets. For exam- ple, no large enough Swedish dataset for coreference resolution exists today. An important part of this work, therefore, is to create a Swedish coreference solver using distantly supervised machine learning, which means creating a Swedish dataset by applying an English coreference solver on an unannotated bilingual corpus, and then using a word-aligner to translate this machine-annotated En- glish dataset to a Swedish dataset, and then training a Swedish model on this dataset. Using Allen NLP:s end-to-end coreference resolution model, both for creating the Swedish dataset and training the Swedish model, this work achieves an F1-score of 0.5. For named entity recognition this work uses the Swedish BERT models released by the Royal Library of Sweden in February 2020 and achieves an overall F1-score of 0.95. To put all of these NLP-models within a single Lan- guage Processing Pipeline, Spacy is used as a unifying framework. Keywords Machine Learning, Natural Language Processing, Relation Extraction, Named Entity Recognition, Coreference resolution, BERT 3 Abstract Natural Language Processing (NLP) är ett stort och aktuellt forskningsområde idag med många praktiska tillämpningar som sentimentanalys, textkategoriser- ing, maskinöversättning och automatisk textsummering. Forskningen är för när- varande mest inriktad på det engelska språket, men många andra språkområ- den försöker komma ikapp. Det här arbetet fokuserar på ett område inom NLP som kallas informationsextraktion, och mer specifikt relationsextrahering, det vill säga att extrahera relationer mellan namngivna entiteter i en text. Vad det här ar- betet försöker göra är att använda olika maskininlärningstekniker för att skapa en svensk Language Processing Pipeline bestående av part-of-speech tagging, de- pendency parsing, named entity recognition och coreference resolution. Denna pipeline är sedan tänkt att användas som en bas for senare relationsextrahering från svenskt arkivmaterial. Den uppenbara svårigheten med detta ligger i att det är ont om stora, annoterade svenska dataset. Till exempel så finns det inget till- räckligt stort svenskt dataset för coreference resolution. En stor del av detta arbete går därför ut på att skapa en svensk coreference solver genom att implementera distantly supervised machine learning, med vilket menas att använda en engelsk coreference solver på ett oannoterat engelskt-svenskt corpus, och sen använda en word-aligner för att översätta detta maskinannoterade engelska dataset till ett svenskt, och sen träna en svensk coreference solver på detta dataset. Det här arbetet använder Allen NLP:s end-to-end coreference solver, både för att skapa det svenska datasetet, och för att träna den svenska modellen, och uppnår en F1-score på 0.5. Vad gäller named entity recognition så använder det här arbetet Kungliga Bibliotekets BERT-modeller som bas, och uppnår genom detta en F1- score på 0.95. Spacy används som ett enande ramverk för att samla alla dessa NLP-komponenter inom en enda pipeline. Nyckelord Maskininlärning, Natural Language Processing, Relationsextrahering, Named En- tity Recognition, Coreference resolution, BERT 4 Contents 1 Introduction 7 1.1 Background . 7 1.2 Problem . 7 1.3 Purpose . 8 1.4 Research Method . 8 1.5 Scope . 8 1.6 Disposition . 8 2 Theoretical Background 11 2.1 Machine Learning . 11 2.2 Natural Language Processing . 15 2.3 Related Works . 19 3 Method 21 3.1 Research Method . 21 3.2 Relation extraction methods . 23 3.3 Technological Methods . 27 4 Training the Spacy Language Processing Pipeline 31 4.1 Datasets . 31 4.2 Part-of-speech tagging . 32 4.3 Dependency parsing . 33 4.4 Named Entity Recognition . 33 4.5 Coreference resolution . 36 5 Results 41 5.1 Part-of-Speech Tagging . 41 5.2 Dependency Parsing . 41 5.3 Named Entity Recognition . 42 5.4 Coreference Resolution . 42 6 Discussion 45 6.1 Natural Language Processing on Swedish Texts . 45 6.2 Discussion of the Results . 47 6.3 Future Work . 48 7 Appendix A - Datasets and Spacy Training Format 49 5 6 1 Introduction Natural Language Processing (NLP) is a sub-field of Artificial Intelligence (AI), lin- guistics and computer science that deals with the ability of computers to draw mean- ing from spoken or written language. The field has been around since the 1950:s, with its ups and downs along the way. In the recent 10-15 years remarkable progress has been made, mainly due to new techniques in the field of machine learning, coupled with the explosion of data made available in the last decade. 1.1 Background The aim of this work is to use machine learning techniques to build a base model for extracting relations between entities in Swedish texts. An entity is some sort of noun-phrase that corresponds to an entity in the world, like a person, an organization, a time expression or a building. An n-ary relation between entities is some sort of relation between n entities in a span of text, such as "Erik sold the Downton Abbey in 1968", which would be a 3-ary relation between Erik, Downton Abbey (which would refer to an actual building in the world) and the year 1968.[12] Before you can extract this relation from an unseen text, several prerequisites needs to be met. For example, we need to recognize and categorize the different entities present in the text, focusing on the types of entities that will take part in the relations we want to extract. But extracting entities isn’t enough, we also need to ex- tract grammatical and syntactical information about the text, as well as some kind of numerical representation for the meaning of words. One other thing that needs to be solved before extracting the relations is coreference-clusters, that is, linking different noun-phrases to each other, such as linking "he" to "Erik" and "computer" to "it" in "Erik has just bought a new computer. He is very happy with it." The project was done in collaboration with Riksarkivet (The Swedish National Archives), and the goal is to use this work’s resulting model as a base to use when implementing relation extraction from archival descriptions. You can then structure these relations in a database, and thereby adding search functionality to the archives. 1.2 Problem To extract relations between entities in a text, information about the text first has to be gathered. For example, the relevant entities needs to be extracted before relations between them can be extracted. But other information has to be extracted as well. For example "Erik owns Fårö Herrgård" and "Erik works at Fårö Herrgård" signifies two different relations even though the named entities (Erik, Fårö Herrgård) are the same. What information about this sentence, and the tokens comprising the sentence, could aid in separating these two, and other kinds of relations? What information is needed to extract the particular relation of interest, and not all relations with the same entity types? Another important condition for this work is that the work is focused on 7 Swedish texts. This presents other problems than if the work was focused on English texts. The Swedish datasets needed for supervised machine learning, if they exists at all, are much smaller than their English counterparts. Thus we arrive at the central problem underlying this work: What are prerequisites to extract entity relations from Swedish texts, and how do you meet these prerequisites given that annotated datasets are scarce? 1.3 Purpose The purpose of this work is to produce a model capable of extracting entity relations from archival descriptions. The intent is that the model, with task-specific adjust- ments, can be used for many different kinds of relation extraction tasks within an on- going project at Riksarkivet to use machine learning techniques to make their archives more accessible. 1.4 Research Method The research methods used for this project were literature studies, both of books on the subject, different types of web-material, and research papers for learning the latest developments. Then, for the actual construction of models, observation combined with experimentation was used. In the case of machine learning this means training a model on only a part of the dataset and keeping the other part unseen for later validation and testing. 1.5 Scope This work was limited to creating a base model for relation extraction. The actual re- lation extraction wasn’t included in the work, but instead, the purpose was to design a model that can serve as a base for many different types of relation extraction tasks. 1.6 Disposition Chapter 2 describes the theory behind the work, and explains the concepts and struc- tures relevant to the task at hand.
Recommended publications
  • 15 Things You Should Know About Spacy

    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.
  • Automatic Correction of Real-Word Errors in Spanish Clinical Texts

    Automatic Correction of Real-Word Errors in Spanish Clinical Texts

    sensors Article Automatic Correction of Real-Word Errors in Spanish Clinical Texts Daniel Bravo-Candel 1,Jésica López-Hernández 1, José Antonio García-Díaz 1 , Fernando Molina-Molina 2 and Francisco García-Sánchez 1,* 1 Department of Informatics and Systems, Faculty of Computer Science, Campus de Espinardo, University of Murcia, 30100 Murcia, Spain; [email protected] (D.B.-C.); [email protected] (J.L.-H.); [email protected] (J.A.G.-D.) 2 VÓCALI Sistemas Inteligentes S.L., 30100 Murcia, Spain; [email protected] * Correspondence: [email protected]; Tel.: +34-86888-8107 Abstract: Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being a real-word error is computed. On the other hand, state-of-the-art approaches make use of deep learning models to learn context by extracting semantic features from text. In this work, a deep learning model were implemented for correcting real-word errors in clinical text. Specifically, a Seq2seq Neural Machine Translation Model mapped erroneous sentences to correct them. For that, different types of error were generated in correct sentences by using rules. Different Seq2seq models were trained and evaluated on two corpora: the Wikicorpus and a collection of three clinical datasets. The medicine corpus was much smaller than the Wikicorpus due to privacy issues when dealing Citation: Bravo-Candel, D.; López-Hernández, J.; García-Díaz, with patient information.
  • ACL 2019 Social Media Mining for Health Applications (#SMM4H)

    ACL 2019 Social Media Mining for Health Applications (#SMM4H)

    ACL 2019 Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Proceedings of the Fourth Workshop August 2, 2019 Florence, Italy c 2019 The Association for Computational Linguistics Order copies of this and other ACL proceedings from: Association for Computational Linguistics (ACL) 209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 [email protected] ISBN 978-1-950737-46-8 ii Preface Welcome to the 4th Social Media Mining for Health Applications Workshop and Shared Task - #SMM4H 2019. The total number of users of social media continues to grow worldwide, resulting in the generation of vast amounts of data. Popular social networking sites such as Facebook, Twitter and Instagram dominate this sphere. According to estimates, 500 million tweets and 4.3 billion Facebook messages are posted every day 1. The latest Pew Research Report 2, nearly half of adults worldwide and two- thirds of all American adults (65%) use social networking. The report states that of the total users, 26% have discussed health information, and, of those, 30% changed behavior based on this information and 42% discussed current medical conditions. Advances in automated data processing, machine learning and NLP present the possibility of utilizing this massive data source for biomedical and public health applications, if researchers address the methodological challenges unique to this media. In its fourth iteration, the #SMM4H workshop takes place in Florence, Italy, on August 2, 2019, and is co-located with the
  • Mapping the Hyponymy Relation of Wordnet Onto

    Mapping the Hyponymy Relation of Wordnet Onto

    Under review as a conference paper at ICLR 2019 MAPPING THE HYPONYMY RELATION OF WORDNET ONTO VECTOR SPACES Anonymous authors Paper under double-blind review ABSTRACT In this paper, we investigate mapping the hyponymy relation of WORDNET to feature vectors. We aim to model lexical knowledge in such a way that it can be used as input in generic machine-learning models, such as phrase entailment predictors. We propose two models. The first one leverages an existing mapping of words to feature vectors (fastText), and attempts to classify such vectors as within or outside of each class. The second model is fully supervised, using solely WORDNET as a ground truth. It maps each concept to an interval or a disjunction thereof. On the first model, we approach, but not quite attain state of the art performance. The second model can achieve near-perfect accuracy. 1 INTRODUCTION Distributional encoding of word meanings from the large corpora (Mikolov et al., 2013; 2018; Pen- nington et al., 2014) have been found to be useful for a number of NLP tasks. These approaches are based on a probabilistic language model by Bengio et al. (2003) of word sequences, where each word w is represented as a feature vector f(w) (a compact representation of a word, as a vector of floating point values). This means that one learns word representations (vectors) and probabilities of word sequences at the same time. While the major goal of distributional approaches is to identify distributional patterns of words and word sequences, they have even found use in tasks that require modeling more fine-grained relations between words than co-occurrence in word sequences.
  • Pairwise Fasttext Classifier for Entity Disambiguation

    Pairwise Fasttext Classifier for Entity Disambiguation

    Pairwise FastText Classifier for Entity Disambiguation a,b b b Cheng Yu , Bing Chu , Rohit Ram , James Aichingerb, Lizhen Qub,c, Hanna Suominenb,c a Project Cleopatra, Canberra, Australia b The Australian National University c DATA 61, Australia [email protected] {u5470909,u5568718,u5016706, Hanna.Suominen}@anu.edu.au [email protected] deterministically. Then we will evaluate PFC’s Abstract performance against a few baseline methods, in- cluding SVC3 with hand-crafted text features. Fi- For the Australasian Language Technology nally, we will discuss ways to improve disambig- Association (ALTA) 2016 Shared Task, we uation performance using PFC. devised Pairwise FastText Classifier (PFC), an efficient embedding-based text classifier, 2 Pairwise Fast-Text Classifier (PFC) and used it for entity disambiguation. Com- pared with a few baseline algorithms, PFC Our Pairwise FastText Classifier is inspired by achieved a higher F1 score at 0.72 (under the the FastText. Thus this section starts with a brief team name BCJR). To generalise the model, description of FastText, and proceeds to demon- we also created a method to bootstrap the strate PFC. training set deterministically without human labelling and at no financial cost. By releasing 2.1 FastText PFC and the dataset augmentation software to the public1, we hope to invite more collabora- FastText maps each vocabulary to a real-valued tion. vector, with unknown words having a special vo- cabulary ID. A document can be represented as 1 Introduction the average of all these vectors. Then FastText will train a maximum entropy multi-class classi- The goal of the ALTA 2016 Shared Task was to fier on the vectors and the output labels.
  • Fasttext.Zip: Compressing Text Classification Models

    Fasttext.Zip: Compressing Text Classification Models

    Under review as a conference paper at ICLR 2017 FASTTEXT.ZIP: COMPRESSING TEXT CLASSIFICATION MODELS Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herve´ Jegou´ & Tomas Mikolov Facebook AI Research fajoulin,egrave,bojanowski,matthijs,rvj,[email protected] ABSTRACT We consider the problem of producing compact architectures for text classifica- tion, such that the full model fits in a limited amount of memory. After consid- ering different solutions inspired by the hashing literature, we propose a method built upon product quantization to store word embeddings. While the original technique leads to a loss in accuracy, we adapt this method to circumvent quan- tization artefacts. Combined with simple approaches specifically adapted to text classification, our approach derived from fastText requires, at test time, only a fraction of the memory compared to the original FastText, without noticeably sacrificing quality in terms of classification accuracy. Our experiments carried out on several benchmarks show that our approach typically requires two orders of magnitude less memory than fastText while being only slightly inferior with respect to accuracy. As a result, it outperforms the state of the art by a good margin in terms of the compromise between memory usage and accuracy. 1 INTRODUCTION Text classification is an important problem in Natural Language Processing (NLP). Real world use- cases include spam filtering or e-mail categorization. It is a core component in more complex sys- tems such as search and ranking. Recently, deep learning techniques based on neural networks have achieved state of the art results in various NLP applications. One of the main successes of deep learning is due to the effectiveness of recurrent networks for language modeling and their application to speech recognition and machine translation (Mikolov, 2012).
  • Question Embeddings for Semantic Answer Type Prediction

    Question Embeddings for Semantic Answer Type Prediction

    Question Embeddings for Semantic Answer Type Prediction Eleanor Bill1 and Ernesto Jiménez-Ruiz2 1, 2 City, University of London, London, EC1V 0HB Abstract. This paper considers an answer type and category prediction challenge for a set of natural language questions, and proposes a question answering clas- sification system based on word and DBpedia knowledge graph embeddings. The questions are parsed for keywords, nouns and noun phrases before word and knowledge graph embeddings are applied to the parts of the question. The vectors produced are used to train multiple multi-layer perceptron models, one for each answer type in a multiclass one-vs-all classification system for both answer cat- egory prediction and answer type prediction. Different combinations of vectors and the effect of creating additional positive and negative training samples are evaluated in order to find the best classification system. The classification system that predict the answer category with highest accuracy are the classifiers trained on knowledge graph embedded noun phrases vectors from the original training data, with an accuracy of 0.793. The vector combination that produces the highest NDCG values for answer category accuracy is the word embeddings from the parsed question keyword and nouns parsed from the original training data, with NDCG@5 and NDCG@10 values of 0.471 and 0.440 respectively for the top five and ten predicted answer types. Keywords: Semantic Web, knowledge graph embedding, answer type predic- tion, question answering. 1 Introduction 1.1 The SMART challenge The challenge [1] provided a training set of natural language questions alongside a sin- gle given answer category (Boolean, literal or resource) and 1-6 given answer types.
  • 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

    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

    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

    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.
  • Linked Data Triples Enhance Document Relevance Classification

    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.
  • Named Entity Extraction for Knowledge Graphs: a Literature Overview

    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.