DOCSLIB.ORG

Automatic Language Identification of Short Texts

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Automatic Language Identification of Short Texts UPTEC F 20043 Examensarbete 30 hp September 2020 Automatic language identification of short texts Anna Avenberg Abstract Automatic language identification of short texts Anna Avenberg Teknisk- naturvetenskaplig fakultet UTH-enheten The world is growing more connected through the use of online communication, exposing software and humans to all the world's Besöksadress: languages. While devices are able to understand and share the raw data Ångströmlaboratoriet Lägerhyddsvägen 1 between themselves and with humans, the information itself is not Hus 4, Plan 0 expressed in a monolithic format. This causes issues both in the human to computer interaction and human to human communication. Automatic Postadress: language identification (LID) is a field within artificial intelligence Box 536 751 21 Uppsala and natural language processing that strives to solve a part of these issues by identifying languages from text, sign language and speech. Telefon: 018 – 471 30 03 One of the challenges is to identify the short pieces of text that can Telefax: be found online, such as messages, comments and posts on social media. 018 – 471 30 00 This is due to the small amount of information they carry. The goal of this thesis has been to build a machine learning model that can identify Hemsida: the language for these short pieces of text. A long short-term memory http://www.teknat.uu.se/student (LSTM) machine learning model was built and benchmarked towards Facebook’s fastText model. The results show how the LSTM model reached an accuracy of around 95% and the fastText model used as comparison reached an accuracy of 97%. The LSTM model struggled more when identifying texts shorter than 50 characters than with longer text. The classification performance of the LSTM model was also relatively poor in cases where languages were similar, like Croatian and Serbian. Both the LSTM model and the fastText model reached accuracy’s above 94% which can be considered high, depending on how it is evaluated. There are however many improvements and possible future work to be considered; looking further into texts shorter than 50 characters, evaluating the model’s softmax output vector values and how to handle similar languages. Handledare: Prashant Singh Ämnesgranskare: Prashant Singh Examinator: Tomas Nyberg UPTEC F 20043 Acknowledgement I would like to express my gratitude to the people that has given me input, helped and supported me throughout the thesis. This thesis would not have been possible without your knowledge, guidance and advice. Many thanks to Prashant Singh for his knowledge and always answering my questions. I also wish to thank the group of people that enriched my lunches with good company and discussions. Lastly I want to thank my family and friends for staying positive and cheering me on. Populärvetenskaplig sammanfattning Idag kommunicerar vi i stor utsträckning genom våra mobiltelefoner och datorer. Kom- munikationen går fort, vi reagerar, kommenterar och svarar såväl vänner och familj som främlingar online. Mycket av det vi skriver om och till varandra är i form av korta texter, såsom ett sms, i en chatt, en kommentar eller ett inlägg på sociala medier. Då internet når människor i hela världen, skrivs mycket av det som förekommer online på flera hundra olika språk. För att webbsidor och olika program ska kunna hantera alla språk är det viktigt för webbsidor och program att automatiskt kunna identifiera språken. Artificiell intelligens, eller AI, är ett ämne som många har hört talas om de senaste åren. AI handlar i stor uträckning om att låta datorer göra uppgifter som tidigare har krävt mänsklig intelligens för att genomföra. Ett delområde inom AI är språkteknologi (engel- ska: natural language processing) varav en del handlar om automatisk språkidentifiering. I det här arbetat har metoder för hur man kan bygga språkigenkänningsmodeller för korta texter studerats och testats. Maskininlärning är en del av AI där det är möjligt att lära en dator olika uppgifter utan att behöva programmera för exakt varje datapunkt, i det här fallet text. Det resulterar i att det går att designa modeller för datorn där det som behövs är data där olika språk är representerade. I arbetet hämtades inlägg från Twitter på olika språk och utifrån den insamlade informa- tionen konstruerades en maskininlärningsmodell. Twitter är en källa till korta texter och inlägg på 9 olika språk hämtades. Språken var engelska, svenska, spanska, portugisiska, ryska, tyska, polska, serbiska och kroatiska. Modellen som konstruerades lyckades korrekt identifiera 95% av den testdata som användes. Modellen som konstruerades jämfördes med modellen fastText som är utvecklad av Facebook. FastText lyckades korrekt identi- fiera 97% av testdata. Det var förvånande att modellerna fick en noggrannhet över 90%, trots att de inte tränats på en stor datamängd. Det här tyder på att de maskininlärn- ingsmodeller som finns tillgängliga att använda gratis online idag har stor potential till att kunna användas i applikationer med goda resultat. Det finns mycket att fortsätta utforska inom området för språkidentifiering av korta texter och hur modeller av liknande slag ska tolkas. Problem som kvarstår är modellens osäkerhet, hur de kortaste texterna ska kunna identifieras samt hur flera språk som liknar varandra bör hanteras av modellen. 4 Contents Populärvetenskaplig sammanfattning 4 1 Introduction 7 1.1 Background . .7 1.2 Objectives . .7 1.2.1 Sub-objectives . .8 2 Theory 9 2.1 Natural language processing . .9 2.1.1 Automatic language identification . .9 2.2 Artificial intelligence . .9 2.3 Machine learning . .9 2.4 Artificial neural networks . 11 2.4.1 Activation functions . 12 2.4.2 Output activation function . 14 2.4.3 Training of neural networks . 16 2.5 Recurrent neural networks . 17 2.5.1 Long short-term memory . 19 2.6 Pre-training of models . 20 2.7 Feature extraction of text . 21 2.7.1 Bag of words . 21 2.7.2 N-grams . 22 2.7.3 Word and character embeddings . 22 2.8 Data extraction . 23 2.8.1 Twitter’s API . 23 2.8.2 Web crawling . 23 3 Method 24 3.1 Dataset . 24 3.2 Preprocessing . 25 3.3 Machine learning model . 26 3.4 Software setup . 28 3.4.1 TensorFlow and Keras . 28 3.5 Experiments . 29 3.5.1 Hyperparameter tuning . 29 3.5.2 Word versus character embeddings . 30 3.5.3 Preprocessing variations . 30 3.5.4 fastText . 30 3.5.5 Two similar languages . 30 4 Results 30 4.1 Hyperparameter tuning . 30 4.2 Word versus character embeddings . 32 4.3 Preprocessing variations . 32 4.4 Final model . 33 5 4.5 fastText . 35 4.6 Two similar languages . 36 5 Discussion 38 5.1 Results . 38 5.2 Errors . 41 5.3 Ethical reflection . 41 5.4 Future work . 42 6 Conclusion 42 References 43 6 1 Introduction More than 4 billion people use the internet today, which corresponds to over 50% of the global population [1]. This means that hundreds of different languages are being used daily online. A sub field within artificial intelligence called natural language processing has made it possible for all of these languages to be identified, translated and communi- cated between each other. The past decade has provided new technology like big data and parallel computing that has made the artificial intelligence ideas possible to implement. Natural language processing includes how to handle language data and get more knowl- edge about a language. Automatic language identification is important for many types of natural language processing tasks today. As the communication online often is done through short pieces of text, a challenge of how to identify the language of these texts has arisen. The thesis covers parts of this field and how machine learning can recognise languages from short texts. Machine learning can be used to create computer models that train on known data and later be able to identify new unknown data. 1.1 Background In recent years it has become more common to come across websites where automatic language identification is used, like Facebook and Google [2]. It is impressive how transla- tion and identification of languages often is accurate. Although sometimes the translation of an expression or saying does not quite match the real meaning. Another example of where languages identification (LID) models are being used is in the search tool bar of websites. When the user types in a couple of words or a sentence, the search tool bar first identify what language the text is written in, before it gives the most up to date results in the same language as typed by the user. Languages continuously develop and change which can become an issue when building models that automatically identify languages. In a time where people communicate online, the changes happen quickly, from one day to another an online community can start to use a completely new word or slang. It is common with badly written text or misspelled words online because of the quick communication on online platforms. The possibility to identify a language of "dirty" and tricky short pieces of text still needs to be explored further. Is it possible to keep up with the constantly changing of languages for machine learning models that identify languages? How easy is it to create a machine learning model to automatically identify different languages from shorter expressions and words containing slang, misspellings or is badly written? Can a model be created to distinguish between possible languages for a short online expression? These questions will be explored in this master thesis. 1.2 Objectives The master thesis objective is to explore how a neural network can be created to identify languages from short texts from internet platforms. With the goal to quickly go from a newly trained network to implementation, automatically.
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • 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.
    [Show full text]
  • Data Driven Value Creation
    DATA DRIVEN VALUE CREATION DATA SCIENCE & ANALYTICS | DATA MANAGEMENT | VISUALIZATION & DATA EXPERIENCE D ONE, Sihlfeldstrasse 58, 8003 Zürich, d-one.ai Agenda Time Content Who 9.00 Introduction Philipp 9.30 Hands-On 1: How to kick-start NLP Jacqueline 10.15 Break / Letting People Catch up All ;-) 10.45 About ELK Philipp 11.00 Hands-On 2: Deep Dive Jacqueline 12.15 Wrap up / Discussion All 2 Today’s hosts Philipp Thomann Jacqueline Stählin Here today & tomorrow morning Here today & Monday till Wednesday 3 Setup Course Git - https://github.com/d-one/NLPeasy-workshop For infos during Workshop - https://tlk.io/amld2020-nlpeasy 4 «Making sense of data is the winning competence in all industries.» 5 Data drives Transformation 6 About ■ Zurich-based, established in 2005 with focus: data-driven value creation ■ 50 data professionals with excellent business and technical understanding & network ■ International & Swiss clients ■ In selected cases: investor for data-driven start-ups 7 Capabilities Business Consulting - Guide on data journey - Use cases, roadmap - Transformation and change - Building capabilities Machine Learning / AI Data Architecture - Ideation, implementation, - Data strategy operationalization Passionate - Enterprise requirements - Algorithms, modelling, NLP & - Information factory - Image recognition, visual analytics Down to - BI and analytics - Text analytics - Data science laboratory earth Data Management Data Experience - Data supply chain - From data to Insights to action - Data integration and modelling - Business report
    [Show full text]
  • Two Models for Named Entity Recognition in Spanish
    Two Models for Named Entity Recognition in Spanish. Submission to the CAPITEL Shared Task at IberLEF 2020 Elena Álvarez-Melladoa aInformation Sciences Institute, University of Southern California Abstract This paper documents two sequence-labeling models for NER in Spanish: a conditional random field model with handcrafted features and a BiLSTM-CRF model with word and character embeddings. Both models were trained and tested using CAPITEL (an annotated corpus of newswire written in European Spanish) and were submitted to the shared task on Spanish NER at IberLEF 2020. The best result was obtained by the CRF model, which produced an F1 score of 84.39 on the test set and was ranked #6 on the shared task. Keywords Spanish NER, CRF, BiLSTM-CRF 1. Introduction and Previous Work Named entity recognition (NER) is the task of extracting relevant spans of text (such as person names, location names, organization names) from a given document. NER has usually been framed as a sequence labeling problem [1]. Consequently, different sequence labeling approaches have been applied to NER, such as hidden Markov models [2], maxent Markov models [3] or conditional random field [4, 5]. More recently, different neural architectures have also been applied to NER [6, 7]. In this paper, we explore two sequence-labeling models to perform NER on CAPITEL, an annotated corpus of journalistic texts written in European Spanish. The explored models are a CRF model with handcrafted features and a BiLSTM-CRF model with word and character embeddings. Both models were submitted to the CAPITEL shared task on Spanish NER at IberLEF 2020 [8].
    [Show full text]
  • Learning Word Vectors for 157 Languages
    Learning Word Vectors for 157 Languages Edouard Grave1;∗ Piotr Bojanowski1;∗ Prakhar Gupta1;2 Armand Joulin1 Tomas Mikolov1 1Facebook AI Research 2EPFL fegrave,bojanowski,ajoulin,[email protected], [email protected] Abstract Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, showing very strong performance compared to previous models. Keywords: word vectors, word analogies, fasttext 1. Introduction are available at https://fasttext.cc. Distributed word representations, also known as word vec- Related work. In previous work, word vectors pre- tors, have been widely used in natural language processing, trained on large text corpora have been released along- leading to state of the art results for many tasks. Publicly side open source implementation of word embedding mod- available models, which are pre-trained on large amounts els. English word vectors trained on a part of the of data, have become a standard tool for many NLP appli- Google News dataset (100B tokens) were published with cations, but are mostly available for English.
    [Show full text]
  • Developing Amaia: a Conversational Agent for Helping Portuguese Entrepreneurs—An Extensive Exploration of Question-Matching Approaches for Portuguese
    information Article Developing Amaia: A Conversational Agent for Helping Portuguese Entrepreneurs—An Extensive Exploration of Question-Matching Approaches for Portuguese José Santos 1 , Luís Duarte 1 , João Ferreira 1 , Ana Alves 1,2 and Hugo Gonçalo Oliveira 1,* 1 CISUC, DEI, University of Coimbra, 3030-290 Coimbra, Portugal; [email protected] (J.S.); [email protected] (L.D.); [email protected] (J.F.); [email protected] (A.A.) 2 Instituto Superior de Engenharia de Coimbra (ISEC), Instituto Politécnico de Coimbra, 3030-199 Coimbra, Portugal * Correspondence: [email protected] Received: 31 July 2020; Accepted: 28 August 2020; Published: 1 September 2020 Abstract: This paper describes how we tackled the development of Amaia, a conversational agent for Portuguese entrepreneurs. After introducing the domain corpus used as Amaia’s Knowledge Base (KB), we make an extensive comparison of approaches for automatically matching user requests with Frequently Asked Questions (FAQs) in the KB, covering Information Retrieval (IR), approaches based on static and contextual word embeddings, and a model of Semantic Textual Similarity (STS) trained for Portuguese, which achieved the best performance. We further describe how we decreased the model’s complexity and improved scalability, with minimal impact on performance. In the end, Amaia combines an IR library and an STS model with reduced features. Towards a more human-like behavior, Amaia can also answer out-of-domain questions, based on a second corpus integrated in the KB. Such interactions are identified with a text classifier, also described in the paper. Keywords: semantic textual similarity; question answering; conversational agents; machine learning; information retrieval; text classification 1.
    [Show full text]
  • Download (435Kb)
    City Research Online City, University of London Institutional Repository Citation: Bill, E. and Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599 (2020). Question embeddings for semantic answer type prediction. Proceedings of the SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge, 2774, pp. 71-80. This is the published version of the paper. This version of the publication may differ from the final published version. Permanent repository link: https://openaccess.city.ac.uk/id/eprint/25566/ Link to published version: Copyright: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to. Reuse: Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. City Research Online: http://openaccess.city.ac.uk/ [email protected] 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.
    [Show full text]
  • A Survey of Embeddings in Clinical Natural Language Processing Kalyan KS, S
    This paper got published in Journal of Biomedical Informatics. Updated version is available @ https://doi.org/10.1016/j.jbi.2019.103323 SECNLP: A Survey of Embeddings in Clinical Natural Language Processing Kalyan KS, S. Sangeetha Text Analytics and Natural Language Processing Lab Department of Computer Applications National Institute of Technology Tiruchirappalli, INDIA. [email protected], [email protected] ABSTRACT Traditional representations like Bag of words are high dimensional, sparse and ignore the order as well as syntactic and semantic information. Distributed vector representations or embeddings map variable length text to dense fixed length vectors as well as capture prior knowledge which can transferred to downstream tasks. Even though embedding has become de facto standard for representations in deep learning based NLP tasks in both general and clinical domains, there is no survey paper which presents a detailed review of embeddings in Clinical Natural Language Processing. In this survey paper, we discuss various medical corpora and their characteristics, medical codes and present a brief overview as well as comparison of popular embeddings models. We classify clinical embeddings into nine types and discuss each embedding type in detail. We discuss various evaluation methods followed by possible solutions to various challenges in clinical embeddings. Finally, we conclude with some of the future directions which will advance the research in clinical embeddings. Keywords: embeddings, distributed representations, medical, natural language processing, survey 1. INTRODUCTION Distributed vector representation or embedding is one of the recent as well as prominent addition to modern natural language processing. Embedding has gained lot of attention and has become a part of NLP researcher’s toolkit.
    [Show full text]