DOCSLIB.ORG

An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems Duygu Altinok 4Com Innovation Center Berlin, Germany [email protected] Abstract Keeping the dialogue state in dialogue systems is a notoriously difficult task. We introduce an ontology-based dialogue manager (OntoDM), a dialogue manager that keeps the state of the conversation, provides a basis for anaphora resolution and drives the conversation via domain ontologies. The banking and finance area promises great potential for disambiguating the context via a rich set of products and specificity of proper nouns, named entities and verbs. We used ontologies both as a knowledge base and a basis for the dialogue manager; the knowledge base component and dialogue manager components coalesce in a sense. Domain knowledge is used to track Entities of Interest, i.e. nodes (classes) of the ontology which happen to be products and services. In this way we also introduced conversation memory and attention in a sense. We finely blended linguistic methods, domain-driven keyword ranking and ​ ​ domain ontologies to create ways of domain-driven conversation. Proposed framework is used in our in-house German language banking and finance chatbots. General challenges of German language processing and finance-banking domain chatbot language models and lexicons are also introduced. This work is still in progress, hence no success metrics have been introduced yet. Keywords: ontology, knowledge base, finance ontology, dialogue management, ontology based dialogue management, ontology based conversation, chatbot, virtual personal assistant, banking and finance virtual assistant, banking and finance chatbot umlaut-to-plain vowel replaced words are also a part of 1. Challenges with German Language chatbot language models. German language processing is inherently challenging in Morphologically rich languages have received general, independent of what the specific NLP task is. The considerable attention from many researchers. Many main challenge is high variability in word forms due to technical papers have been published to highlight the inflections and compound words. inherent technical difficulties in statistical methods e.g. Finance domain lexicons include many compound words MT, ASR-TTS, language models, text classification; just like other technical domains in German. Here are practical solutions are offered in (Mikolov et al., 2016). some examples from our in-house banking and finance We overcome the challenges of rich German morphology using DEMorphy, an open source German morphological lexicon: 1 analyzer and recognizer . Throughout our work, all Verfügungsberechtigung power to draw from an account lemmatizing and morphological analysis tasks are done by Sparkonto savings account DEMorphy. Girokonto checking account Steuernummer tax ID 2. Introduction Zahlungsverkehr payment transactions Keeping dialogue state in conversational interfaces is a Zahlungsverkehrsraum payment transactions area notoriously difficult task. Dialogue systems, also known Onlinebanking online banking as chatbots, virtual assistants and conversational interfaces Hypothekendarlehen mortgage are already used in a broad set of applications, from psychological support2 to HR, customer care and Nouns, adjectives and verbs can be inflected according to entertainment. gender, number and person. Rich word forms can pose a Dialogue systems can be classified into goal-driven challenge language understanding components. In this systems (e.g. flight booking, restaurant reservation) vs paper, we focus on dialogue management. However, one open-domain systems (e.g. psychological support, should keep in mind that input to dialogue management language learning and medical aid). As dialogue systems components are provided by natural language has gained attention, research interest in training natural understanding components. conversation systems from large volumes of user data has Another practical issue in everyday written language is grown. Goal-driven systems admit slot-filling and hand the umlaut (mutated vowels). Everyday informal written ​ crafted rules, which is reliable but restrictive in the text includes umlauts replaced by their plain counterparts conversation (basically the user has to choose one of i.e. “Madchen, uber, schon” rather than “Mädchen, über, available options). Open domain conversational systems, schön” etc. Especially in conversational interfaces, usage based on generative probabilistic models attracted of umlauts reduce significantly due to English layout keyboards or just being lax about punctuation while 1 https://github.com/DuyguA/DEMorphy ​ typing quickly on a smartphone. In our opinion, 2 https://www.wysa.io ​ attention from many researchers, due these limits for highly specific domains such as medicine, law and goal-oriented systems (Serban et al., 2015; Li et al., online-shopping. 2017). Banking and finance conversations, either seeking financial advice or inquiring banking products; aim to One problem with conversation is maintaining the get information rather than to accomplish a goal. Hence dialogue state. This comprises of what the user said and throughout this work, conversations are not goal-oriented how the chatbot answered, what we’re talking about and but rather domain-driven. Users usually do not aim to ​ which pieces of information are relevant to generating the achieve a well-defined goal, i.e. book a table at a current answer. Kumar et al. (2017) introduced neural restaurant or book a flight. The financial chat is mostly networks with memory and attention (DMN). Done up to about getting information about rates, prices, investment here DMN includes episodic memory and an attention instruments and sometimes about picking a suitable option module plus to a recurrent encoder decoder. DMN first i.e. purchase advice. Purchase rarely happens immediately ​ ​ ​ computes question representation. Then the question after the advice. The banking chat can include both asking representation triggers the attention process. Finally the for information about account types, credit card types, memory module can reason the answer from all relevant their yearly fees and rates i.e. the banking products; or information. making money transfer, asking for account balance, However, purely statistical approach has some drawbacks: viewing account activity i.e. achieving a certain goal. We ● statistical frameworks need huge training sets. will address these issues in next chapters. ​ Especially frameworks with many statistical components such as DMN, have a great number 3.2 Ontology-based Dialogues of parameters and are vulnerable to sparseness Ontology-based conversating is indeed a way of problems. domain-driven conversation. We used ontologies in our ● Anaphora resolution is implicit. The anaphora work for two purposes: resolutions go into neural network as implicit ● to store the knowledge parameters, there’s no direct easy way to see how ● to navigate through the domain the resolutions worked. Answers come through at The knowledge base component takes part in many least two distinct statistical layers, one encoder dialogue systems. After the NLU component turns queries and one decoder at least. Thus there is no easy into a logical form, next step is to interrogate the way to understand why a specific answer is knowledge base for the answer generation. In banking and generated and how the anaphora resolution finance domain ontologies, one potentially needs to store contributed to the generation. ● a range of banking products: credit, credit card, This paper addresses the dialog management. We will debit card… describe domain-driven ways to ● attributes of these products: general conditions, ● keep the conversation memory, both the user and rates and fees the bot side ● range of banking services: ATM, money ● make the anaphora resolution transfer... ● generate knowledge-based answers ● attributes of the banking services: ATM points, ● possibly contribute to what to say next ​ branch addresses... ● integrate linguistic features into the context For instance, in following conversations (Fig.1 & 2) it is NLU and answer generation modules will not be not possible to generate an answer without knowing the ​ ​ considered in detail in this paper. The focus is on how product. The knowledge base module provides ontologies can be used to generate natural conversations. information to the answer generation module. However we will present the outputs and presentations for clarity. The goal is here to improve quality of conversations via domain knowledge. This work is still in User: Was kostet die Bestellung im Internet? (How much an online progress, hence we were not able to include performance purchase costs?) Bot: Es entstehen keine Kosten für Sie.. (It costs you nothing.) metrics yet, given the difficult nature of evaluation of dialogue systems in general. Figure 1: Dialogue Example 3. Proposed Framework User: Haben Sie einen Geldautomaten in Berlin-Tiergarten? (Do you have an ATM in Berlin-Tiergarten?) 3.1 The Domain Bot: Ja, …….. (Yes, ….) This paper describes the methodology that is used in our Figure 2: Dialogue Example in-house banking and finance chatbots. We chose the banking and finance domain due to rich set of products In our work the ontology also drives the conversation: and specificity of proper nouns, named entities and verbs; keeps the context, provides a basis to the anaphora ​ ​ high potential for disambiguate the context and drive the resolution and possibly produces
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]