DOCSLIB.ORG

Pairwise Fasttext Classifier for Entity Disambiguation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read 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. Fast Text disambiguate two person or organisation entities has been shown to train quickly and achieve pre- (Chisholm et al., 2016). The real-world motiva- diction performance comparable to Recurrent tion for the Task includes gathering information Neural Network embedding model for text classi- about potential clients, and law enforcement. fication (Joulin et al., 2016). We designed a Pairwise FastText Classifier (PFC) to disambiguate the entities (Chisholm et 2.2 PFC al., 2016). The major source of inspiration for 2 PFC is similar to FastText except that PFC takes PFC came from FastText algorithm which two inputs in the form of a list of vocabulary IDs, achieved quick and accurate text classification because disambiguation requires two URL inputs. (Joulin et al., 2016). We also devised a method to We specify that each of them is passed into the augment our training examples deterministically, same embedding matrix. If each entity is repre- and released all source code to the public. sented by a d dimensional vector, then we can The rest of the paper will start with PFC and a concatenate them, and represent the two entities mixture model based on PFC, and proceeds to pre- sent our solution to augment the labelled dataset 1 All source code can be downloaded from: 2 The original paper of FastText used the typography https://github.com/projectcleopatra/PFC fastText 3 SVC: Support vector classification Cheng Yu, Bing Chu, Rohit Ram, James Aichinger, Lizhen Qu and Hanna Suominen. 2016. Pairwise FastText Classifier for Entity Disambiguation. In Proceedings of Australasian Language Technology Association Workshop, pages 175−179. by a 2d dimensional vector. Then we train a max- imum entropy classifier based on the concatenated vector. The diagram of the model is in Figure 1. Figure 1: PFC model. W1 and W2 are trainable weights. 2.3 The PFC Mixture Model Figure 2: The PFC Mixture Model. The previous section introduces word-embed- 3 Augmenting More Training Examples ding-based PFC. In order to improve disambigua- Deterministically tion performance, we built a mixture model based on various PFC sub-models: Besides word-em- Embedding-models tend to have a large number bedding-based PFC, we also trained character- of parameters. Our word-embedding matrix has embedding-based PFC, which includes one uni- over 3700 rows, and thus it is natural to brain- character PFC, and one bi-character PFC. In the storm ways to augment the training set to prevent following subsections, we will first briefly explain overfitting. character-embedding-based PFC, and then show We created a method to harvest additional the Mixture model. training examples deterministically without the need for human labelling, and the data can be ac- 2.3.1 Character-Embedding-Based PFCs quired at no additional cost. Character-embedding-based PFC models typi- 3.1 Acquiring Training Examples for the cally have fewer parameters than word-embed- Negative Class4 ding-based PFC, and thus reducing the probability of overfitting. To acquire URL pairs that refer to different peo- Uni-character embedding maps each character ple, we wrote a scraping bot that visits LinkedIn, in the URL and search engine snippet into a 13- and grabs hyperlinks in a section called “People dimensional vector, take the average of an input that are similar to the person”, where LinkedIn document, concatenate the two documents, and recommends professionals that have similar to the then train a maximum entropy classification on current profile that we are browsing. LinkedIn re- top of the concatenated vectors. stricts the number of profiles we can browse in a Bi-character embedding model has a moving given month unless the user is a Premium user, so window of two characters and mapped every such we upgraded our LinkedIn account for scraping two characters into a 16-dimensional vector. purpose. We used the LinkedIn URLs provided to Our implementation of the character-embed- us in the training samples, and grabbed similar ding based PFC model includes only lowercase LinkedIn profiles, which ended up with about 850 English letters and space. After converting all let- profiles, with some of the LinkedIn URLs no ters to lowercase, other characters are simply longer up to date. skipped and ignored. 3.2 Acquiring Training Examples for the 2.3.2 Mixing PFC Sub-models Positive Class The mixture model has two phases. In phase one, To acquire training examples of different social we train each sub-model independently. In phase media profiles that belong to the same person, we 2, we train a simple binary classifier based on the used examples from about.me. About.me is a probability output of each individual PFC. The di- platform where people could create a personal agram of the PFC mixture model is shown in Fig- page showing their professional portfolios and ure 2. links to various social media sites. We wrote a scraping bot that visits about.me/discover, where the site showcases their users, and clicks open 4 In the Shared Task, if a pair of URL entities refer to differ- class. if a pair of URL entities refer to the same persons or ent persons or organisations, the pair belongs to the negative organisations, the pair belongs to the positive class. 176 each user, acquires their social media links, and manually selected the following text features. Ex- randomly selects two as a training example. For planation of these features is available in Appen- example, for someone with 5 social media pro- dix-A. files, including Facebook, Twitter, LinkedIn, Pin- LSTM Word-Embedding: We passed each terest, and Google+, the bot can generate (5, 2) = document token sequentially using word embed- 10 training examples. ding into an LSTM layer with 50 LSTM units (Brownlee, 2016) (Goodfellow et al., 2016), con- 4 Experimental Setup catenated the two output vectors, and trained a maximum entropy classifier on top of it. To re- Using the training data provided by the Organ- duce overfitting, we added dropout layers with the iser and data acquired using the method men- dropout parameter set to 0.2 (Zaremba, Sutskever, tioned in Section 3, we evaluated the perfor- & Vinyals, 2014). mance of our PFC and PFC Mixture against a Neural Tensor Network: Inspired by Socher few baseline models. et al., by passing a pair of documents represented 4.1 Datasets in vector form into a tensor, we built a relationship classifier based on the architecture in the paper The organiser prepared 200 labelled pairs of train- (Socher et al., 2013). Document vectors are calcu- ing samples and 200 unlabelled test samples lated from pre-trained Google embedding word (Hachey, 2016). All baseline methods and PFC vectors. methods are trained using the original 200 URL pairs. The only exception is “PFC with augmented 5 Results and Discussion dataset”, which uses the method in the previous section to acquire 807 negative class URL pairs, The experimental results from the setup is sum- and 891 positive class URL pairs. marised in the table. Method F1 F1 F1 4.2 Pre-Processing Public Pri- To- vate 5 Text content for the PFC comes from the search tal engine snippet file provided by the Organiser and PFC- PFC with 0.75 0.64 0.69 based Word-Embed- text scraped from the URLs provided by the train- ding ing examples. Unknown words in the test set are PFC Mixture 0.74 0.71 0.72 represented by a special symbol. Model 4.3 Baselines PFC with 0.65 0.69 0.67 augmented The reason we choose a few baseline models is dataset that there is no gold-standard baseline model for Base- Neural tensor 0.67 0.6 0.64 URL entity disambiguation. Baseline models are line network explained as followed. SVC using 0.75 0.69 0.72 Word-Embedding with Pre-Trained Vec- hand-selected tors: The training corpus Google comes from features LSTM word- 0.51 0.53 0.52 News Articles (Mikolov et al., 2013). For each embedding URL entity, we calculated the mean vector of the search result snippet text by using pre-trained Table 1: Result comparison.
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]
  • 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]
  • Fasttext-Based Intent Detection for Inflected Languages †
    information Article FastText-Based Intent Detection for Inflected Languages † Kaspars Balodis 1,2,* and Daiga Deksne 1 1 Tilde, Vien¯ıbas Gatve 75A, LV-1004 R¯ıga, Latvia; [email protected] 2 Faculty of Computing, University of Latvia, Rain, a blvd. 19, LV-1586 R¯ıga, Latvia * Correspondence: [email protected] † This paper is an extended version of our paper published in 18th International Conference AIMSA 2018, Varna, Bulgaria, 12–14 September 2018. Received: 15 January 2019; Accepted: 25 April 2019; Published: 1 May 2019 Abstract: Intent detection is one of the main tasks of a dialogue system. In this paper, we present our intent detection system that is based on fastText word embeddings and a neural network classifier. We find an improvement in fastText sentence vectorization, which, in some cases, shows a significant increase in intent detection accuracy. We evaluate the system on languages commonly spoken in Baltic countries—Estonian, Latvian, Lithuanian, English, and Russian. The results show that our intent detection system provides state-of-the-art results on three previously published datasets, outperforming many popular services. In addition to this, for Latvian, we explore how the accuracy of intent detection is affected if we normalize the text in advance. Keywords: intent detection; word embeddings; dialogue system 1. Introduction and Related Work Recent developments in deep learning have made neural networks the mainstream approach for a wide variety of tasks, ranging from image recognition to price forecasting to natural language processing. In natural language processing, neural networks are used for speech recognition and generation, machine translation, text classification, named entity recognition, text generation, and many other tasks.
    [Show full text]