DOCSLIB.ORG

Learning Word Embeddings for Low-Resource Languages by PU Learning

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Learning Word Embeddings for Low-Resource Languages by PU Learning Learning Word Embeddings for Low-resource Languages by PU Learning Chao Jiang Hsiang-Fu Yu University of Virginia Amazon [email protected] [email protected] Cho-Jui Hsieh Kai-Wei Chang University of California Davis University of California Los Angeles [email protected] [email protected] Abstract they have similar contexts. To learn a good word embedding, most approaches assume a large col- Word embedding is a key component in many lection of text is freely available, such that the es- downstream applications in processing natu- timation of word co-occurrences is accurate. For ral languages. Existing approaches often as- example, the Google Word2Vec model (Mikolov sume the existence of a large collection of text for learning effective word embedding. et al., 2013a) is trained on the Google News However, such a corpus may not be avail- dataset, which contains around 100 billion to- able for some low-resource languages. In kens, and the GloVe embedding (Pennington et al., this paper, we study how to effectively learn 2014) is trained on a crawled corpus that contains a word embedding model on a corpus with 840 billion tokens in total. However, such an as- only a few million tokens. In such a situa- sumption may not hold for low-resource languages tion, the co-occurrence matrix is sparse as the such as Inuit or Sindhi, which are not spoken by co-occurrences of many word pairs are unob- many people or have not been put into a digital served. In contrast to existing approaches of- ten only sample a few unobserved word pairs format. For those languages, usually, only a lim- as negative samples, we argue that the zero ited size corpus is available. Training word vectors entries in the co-occurrence matrix also pro- under such a setting is a challenging problem. vide valuable information. We then design One key restriction of the existing approaches a Positive-Unlabeled Learning (PU-Learning) is that they often mainly rely on the word pairs approach to factorize the co-occurrence matrix that are observed to co-occur on the training data. and validate the proposed approaches in four different languages. When the size of the text corpus is small, most word pairs are unobserved, resulting in an ex- 1 Introduction tremely sparse co-occurrence matrix (i.e., most en- tries are zero)1. For example, the text82 corpus Learning word representations has become a has about 17,000,000 tokens and 71,000 distinct fundamental problem in processing natural lan- words. The corresponding co-occurrence matrix guages. These semantic representations, which has more than five billion entries, but only about map a word into a point in a linear space, have 45,000,000 are non-zeros (observed on the train- been widely applied in downstream applications, ing corpus). Most existing approaches, such as including named entity recognition (Guo et al., Glove and Skip-gram, cannot handle a vast num- 2014), document ranking (Nalisnick et al., 2016), ber of zero terms in the co-occurrence matrix; sentiment analysis (Irsoy and Cardie, 2014), ques- therefore, they only sub-sample a small subset of tion answering (Antol et al., 2015), and image cap- zero entries during the training. tioning (Karpathy and Fei-Fei, 2015). In contrast, we argue that the unobserved word Over the past few years, various approaches pairs can provide valuable information for train- have been proposed to learn word vectors (e.g., ing a word embedding model, especially when (Pennington et al., 2014; Mikolov et al., 2013a; the co-occurrence matrix is very sparse. Inspired Levy and Goldberg, 2014b; Ji et al., 2015)) based 1 on co-occurrence information between words ob- Note that the zero term can mean either the pairs of words cannot co-occur or the co-occurrence is not observed served on the training corpus. The intuition behind in the training corpus. this is to represent words with similar vectors if 2http://mattmahoney.net/dc/text8.zip 1024 Proceedings of NAACL-HLT 2018, pages 1024–1034 New Orleans, Louisiana, June 1 - 6, 2018. c 2018 Association for Computational Linguistics by the success of Positive-Unlabeled Learning these approaches are often less flexible. Besides, (PU-Learning) in collaborative filtering applica- directly factorizing the co-occurrence matrix may tions (Pan et al., 2008; Hu et al., 2008; Pan and cause the frequent words dominating the training Scholz, 2009; Qin et al., 2010; Paquet and Koenig- objective. stein, 2013; Hsieh et al., 2015), we design an algo- In the past decade, various approaches have rithm to effectively learn word embeddings from been proposed to improve the training of word em- both positive (observed terms) and unlabeled (un- beddings. For example, instead of factorizing the observed/zero terms) examples. Essentially, by co-occurrence count matrix, Bullinaria and Levy using the square loss to model the unobserved (2007); Levy and Goldberg(2014b) proposed to terms and designing an efficient update rule based factorize point-wise mutual information (PMI) and on linear algebra operations, the proposed PU- positive PMI (PPMI) matrices as these metrics Learning framework can be trained efficiently and scale the co-occurrence counts (Bullinaria and effectively. Levy, 2007; Levy and Goldberg, 2014b). Skip- We evaluate the performance of the proposed gram model with negative-sampling (SGNS) and approach in English3 and other three resource- Continuous Bag-of-Words models (Mikolov et al., scarce languages. We collected unlabeled lan- 2013b) were proposed for training word vectors on guage corpora from Wikipedia and compared the a large scale without consuming a large amount of proposed approach with popular approaches, the memory. GloVe (Pennington et al., 2014) is pro- Glove and the Skip-gram models, for training posed as an alternative to decompose a weighted word embeddings. The experimental results show log co-occurrence matrix with a bias term added that our approach significantly outperforms the to each word. Very recently, WordRank model baseline models, especially when the size of the (Ji et al., 2015) has been proposed to minimize training corpus is small. a ranking loss which naturally fits the tasks re- Our key contributions are summarized below. quiring ranking based evaluation metrics. Stratos et al.(2015) also proposed CCA (canonical cor- We propose a PU-Learning framework for • relation analysis)-based word embedding which learning word embedding. shows competitive performance. All these ap- We tailor the coordinate descent algo- proaches focus on the situations where a large text • rithm (Yu et al., 2017b) for solving the cor- corpus is available. responding optimization problem. Positive and Unlabeled (PU) Learning: Pos- Our experimental results show that PU- itive and Unlabeled (PU) learning (Li and Liu, • Learning improves the word embedding 2005) is proposed for training a model when the training in the low-resource setting. positive instances are partially labeled and the un- labeled instances are mostly negative. Recently, 2 Related work PU learning has been used in many classification and collaborative filtering applications due to the Learning word vectors. The idea of learning nature of “implicit feedback” in many recommen- word representations can be traced back to La- dation systems—users usually only provide posi- tent Semantic Analysis (LSA) (Deerwester et al., tive feedback (e.g., purchases, clicks) and it is very 1990) and Hyperspace Analogue to Language hard to collect negative feedback. (HAL) (Lund and Burgess, 1996), where word vectors are generated by factorizing a word- To resolve this problem, a series of PU matrix document and word-word co-occurrence matrix, completion algorithms have been proposed (Pan respectively. Similar approaches can also be ex- et al., 2008; Hu et al., 2008; Pan and Scholz, 2009; tended to learn other types of relations between Qin et al., 2010; Paquet and Koenigstein, 2013; words (Yih et al., 2012; Chang et al., 2013) or enti- Hsieh et al., 2015; Yu et al., 2017b). The main ties (Chang et al., 2014). However, due to the lim- idea is to assign a small uniform weight to all itation of the use of principal component analysis, the missing or zero entries and factorize the corre- sponding matrix. Among them, Yu et al.(2017b) 3 Although English is not a resource-scarce language, we proposed an efficient algorithm for matrix factor- simulate the low-resource setting in an English corpus. In this way, we leverage the existing evaluation methods to evaluate ization with PU-learning, such that the weighted the proposed approach. matrix is constructed implicitly. In this paper, we 1025 , vocabulary of central and context words chance they appear together in a local context win- W C m, n vocabulary sizes dow) and their marginal probabilities (the chance k dimension of word vectors they appear independently) (Levy and Goldberg, W, H m k and n k latent matrices 2014b). More specifically, each entry of PMI ma- × × Cij weight for the (i, j) entry trix can be defined by Aij value of the PPMI matrix Q value of the co-occurrence matrix Pˆ(w, c) ij PMI(w, c) = log , (1) wi, hj i-th row of W and j-th row of H Pˆ(w) Pˆ(c) b, bˆ bias term · λi, λj regularization parameters where Pˆ(w), Pˆ(c) and Pˆ(w, c) are the the fre- the size of a set quency of word w, word c, and word pairs (w, c), | · | Ω Set of possible word-context pairs respectively. The PMI matrix can be computed Ω+ Set of observed word-context pairs based on the co-occurrence counts of word pairs, Ω− Set of unobserved word-context pairs and it is an information-theoretic association mea- sure which effectively eliminates the big differ- Table 1: Notations.
Load more

Recommended publications
  • Classifying Relevant Social Media Posts During Disasters Using Ensemble of Domain-Agnostic and Domain-Specific Word Embeddings
    AAAI 2019 Fall Symposium Series: AI for Social Good 1 Classifying Relevant Social Media Posts During Disasters Using Ensemble of Domain-agnostic and Domain-specific Word Embeddings Ganesh Nalluru, Rahul Pandey, Hemant Purohit Volgenau School of Engineering, George Mason University Fairfax, VA, 22030 fgn, rpandey4, [email protected] Abstract to provide relevant intelligence inputs to the decision-makers (Hughes and Palen 2012). However, due to the burstiness of The use of social media as a means of communication has the incoming stream of social media data during the time of significantly increased over recent years. There is a plethora of information flow over the different topics of discussion, emergencies, it is really hard to filter relevant information which is widespread across different domains. The ease of given the limited number of emergency service personnel information sharing has increased noisy data being induced (Castillo 2016). Therefore, there is a need to automatically along with the relevant data stream. Finding such relevant filter out relevant posts from the pile of noisy data coming data is important, especially when we are dealing with a time- from the unconventional information channel of social media critical domain like disasters. It is also more important to filter in a real-time setting. the relevant data in a real-time setting to timely process and Our contribution: We provide a generalizable classification leverage the information for decision support. framework to classify relevant social media posts for emer- However, the short text and sometimes ungrammatical nature gency services. We introduce the framework in the Method of social media data challenge the extraction of contextual section, including the description of domain-agnostic and information cues, which could help differentiate relevant vs.
    [Show full text]
  • Injection of Automatically Selected Dbpedia Subjects in Electronic
    Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hospitalization Prediction Raphaël Gazzotti, Catherine Faron Zucker, Fabien Gandon, Virginie Lacroix-Hugues, David Darmon To cite this version: Raphaël Gazzotti, Catherine Faron Zucker, Fabien Gandon, Virginie Lacroix-Hugues, David Darmon. Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hos- pitalization Prediction. SAC 2020 - 35th ACM/SIGAPP Symposium On Applied Computing, Mar 2020, Brno, Czech Republic. 10.1145/3341105.3373932. hal-02389918 HAL Id: hal-02389918 https://hal.archives-ouvertes.fr/hal-02389918 Submitted on 16 Dec 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hospitalization Prediction Raphaël Gazzotti Catherine Faron-Zucker Fabien Gandon Université Côte d’Azur, Inria, CNRS, Université Côte d’Azur, Inria, CNRS, Inria, Université Côte d’Azur, CNRS, I3S, Sophia-Antipolis, France I3S, Sophia-Antipolis, France
    [Show full text]
  • Information Extraction Based on Named Entity for Tourism Corpus
    Information Extraction based on Named Entity for Tourism Corpus Chantana Chantrapornchai Aphisit Tunsakul Dept. of Computer Engineering Dept. of Computer Engineering Faculty of Engineering Faculty of Engineering Kasetsart University Kasetsart University Bangkok, Thailand Bangkok, Thailand [email protected] [email protected] Abstract— Tourism information is scattered around nowa- The ontology is extracted based on HTML web structure, days. To search for the information, it is usually time consuming and the corpus is based on WordNet. For these approaches, to browse through the results from search engine, select and the time consuming process is the annotation which is to view the details of each accommodation. In this paper, we present a methodology to extract particular information from annotate the type of name entity. In this paper, we target at full text returned from the search engine to facilitate the users. the tourism domain, and aim to extract particular information Then, the users can specifically look to the desired relevant helping for ontology data acquisition. information. The approach can be used for the same task in We present the framework for the given named entity ex- other domains. The main steps are 1) building training data traction. Starting from the web information scraping process, and 2) building recognition model. First, the tourism data is gathered and the vocabularies are built. The raw corpus is used the data are selected based on the HTML tag for corpus to train for creating vocabulary embedding. Also, it is used building. The data is used for model creation for automatic for creating annotated data.
    [Show full text]
  • A Second Look at Word Embeddings W4705: Natural Language Processing
    A second look at word embeddings W4705: Natural Language Processing Fei-Tzin Lee October 23, 2019 Fei-Tzin Lee Word embeddings October 23, 2019 1 / 39 Overview Last time... • Distributional representations (SVD) • word2vec • Analogy performance Fei-Tzin Lee Word embeddings October 23, 2019 2 / 39 Overview This time • Homework-related topics • Non-homework topics Fei-Tzin Lee Word embeddings October 23, 2019 3 / 39 Overview Outline 1 GloVe 2 How good are our embeddings? 3 The math behind the models? 4 Word embeddings, new and improved Fei-Tzin Lee Word embeddings October 23, 2019 4 / 39 GloVe Outline 1 GloVe 2 How good are our embeddings? 3 The math behind the models? 4 Word embeddings, new and improved Fei-Tzin Lee Word embeddings October 23, 2019 5 / 39 GloVe A recap of GloVe Our motivation: • SVD places too much emphasis on unimportant matrix entries • word2vec never gets to look at global co-occurrence statistics Can we create a new model that balances the strengths of both of these to better express linear structure? Fei-Tzin Lee Word embeddings October 23, 2019 6 / 39 GloVe Setting We'll use more or less the same setting as in previous models: • A corpus D • A word vocabulary V from which both target and context words are drawn • A co-occurrence matrix Mij , from which we can calculate conditional probabilities Pij = Mij =Mi Fei-Tzin Lee Word embeddings October 23, 2019 7 / 39 GloVe The GloVe objective, in overview Idea: we want to capture not individual probabilities of co-occurrence, but ratios of co-occurrence probability between pairs (wi ; wk ) and (wj ; wk ).
    [Show full text]
  • Cw2vec: Learning Chinese Word Embeddings with Stroke N-Gram Information
    The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information Shaosheng Cao,1,2 Wei Lu,2 Jun Zhou,1 Xiaolong Li1 1 AI Department, Ant Financial Services Group 2 Singapore University of Technology and Design {shaosheng.css, jun.zhoujun, xl.li}@antfin.com [email protected] Abstract We propose cw2vec, a novel method for learning Chinese word embeddings. It is based on our observation that ex- ploiting stroke-level information is crucial for improving the learning of Chinese word embeddings. Specifically, we de- sign a minimalist approach to exploit such features, by us- ing stroke n-grams, which capture semantic and morpholog- ical level information of Chinese words. Through qualita- tive analysis, we demonstrate that our model is able to ex- Figure 1: Radical v.s. components v.s. stroke n-gram tract semantic information that cannot be captured by exist- ing methods. Empirical results on the word similarity, word analogy, text classification and named entity recognition tasks show that the proposed approach consistently outperforms Bojanowski et al. 2016; Cao and Lu 2017). While these ap- state-of-the-art approaches such as word-based word2vec and proaches were shown effective, they largely focused on Eu- GloVe, character-based CWE, component-based JWE and ropean languages such as English, Spanish and German that pixel-based GWE. employ the Latin script in their writing system. Therefore the methods developed are not directly applicable to lan- guages such as Chinese that employ a completely different 1. Introduction writing system. Word representation learning has recently received a sig- In Chinese, each word typically consists of less charac- nificant amount of attention in the field of natural lan- ters than English1, where each character conveys fruitful se- guage processing (NLP).
    [Show full text]
  • Effects of Pre-Trained Word Embeddings on Text-Based Deception Detection
    2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress Effects of Pre-trained Word Embeddings on Text-based Deception Detection David Nam, Jerin Yasmin, Farhana Zulkernine School of Computing Queen’s University Kingston, Canada Email: {david.nam, jerin.yasmin, farhana.zulkernine}@queensu.ca Abstract—With e-commerce transforming the way in which be wary of any deception that the reviews might present. individuals and businesses conduct trades, online reviews have Deception can be defined as a dishonest or illegal method become a great source of information among consumers. With in which misleading information is intentionally conveyed 93% of shoppers relying on online reviews to make their purchasing decisions, the credibility of reviews should be to others for a specific gain [2]. strongly considered. While detecting deceptive text has proven Companies or certain individuals may attempt to deceive to be a challenge for humans to detect, it has been shown consumers for various reasons to influence their decisions that machines can be better at distinguishing between truthful about purchasing a product or a service. While the motives and deceptive online information by applying pattern analysis for deception may be hard to determine, it is clear why on a large amount of data. In this work, we look at the use of several popular pre-trained word embeddings (Word2Vec, businesses may have a vested interest in producing deceptive GloVe, fastText) with deep neural network models (CNN, reviews about their products.
    [Show full text]
  • Smart Ubiquitous Chatbot for COVID-19 Assistance with Deep Learning Sentiment Analysis Model During and After Quarantine
    Smart Ubiquitous Chatbot for COVID-19 Assistance with Deep learning Sentiment Analysis Model during and after quarantine Nourchène Ouerhani ( [email protected] ) University of Manouba, National School of Computer Sciences, RIADI Laboratory, 2010, Manouba, Tunisia Ahmed Maalel University of Manouba, National School of Computer Sciences, RIADI Laboratory, 2010, Manouba, Tunisia Henda Ben Ghézala University of Manouba, National School of Computer Sciences, RIADI Laboratory, 2010, Manouba, Tunisia Soulaymen Chouri Vialytics Lautenschlagerstr Research Article Keywords: Chatbot, COVID-19, Natural Language Processing, Deep learning, Mental Health, Ubiquity Posted Date: June 25th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-33343/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Noname manuscript No. (will be inserted by the editor) Smart Ubiquitous Chatbot for COVID-19 Assistance with Deep learning Sentiment Analysis Model during and after quarantine Nourch`ene Ouerhani · Ahmed Maalel · Henda Ben Gh´ezela · Soulaymen Chouri Received: date / Accepted: date Abstract The huge number of deaths caused by the posed method is a ubiquitous healthcare service that is novel pandemic COVID-19, which can affect anyone presented by its four interdependent modules: Informa- of any sex, age and socio-demographic status in the tion Understanding Module (IUM) in which the NLP is world, presents a serious threat for humanity and so- done, Data Collector Module (DCM) that collect user’s ciety. At this point, there are two types of citizens, non-confidential information to be used later by the Ac- those oblivious of this contagious disaster’s danger that tion Generator Module (AGM) that generates the chat- could be one of the causes of its spread, and those who bots answers which are managed through its three sub- show erratic or even turbulent behavior since fear and modules.
    [Show full text]
  • A Comparison of Word Embeddings and N-Gram Models for Dbpedia Type and Invalid Entity Detection †
    information Article A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection † Hanqing Zhou *, Amal Zouaq and Diana Inkpen School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa ON K1N 6N5, Canada; [email protected] (A.Z.); [email protected] (D.I.) * Correspondence: [email protected]; Tel.: +1-613-562-5800 † This paper is an extended version of our conference paper: Hanqing Zhou, Amal Zouaq, and Diana Inkpen. DBpedia Entity Type Detection using Entity Embeddings and N-Gram Models. In Proceedings of the International Conference on Knowledge Engineering and Semantic Web (KESW 2017), Szczecin, Poland, 8–10 November 2017, pp. 309–322. Received: 6 November 2018; Accepted: 20 December 2018; Published: 25 December 2018 Abstract: This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We tackle two challenges: (a) the detection of entity types, which can be used to detect invalid DBpedia types and assign DBpedia types for type-less entities; and (b) the detection of invalid entities in the resource description of a DBpedia entity. Our results show that entity embeddings outperform n-gram models for type and entity detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution. Keywords: semantic web; DBpedia; entity embedding; n-grams; type identification; entity identification; data mining; machine learning 1. Introduction The Semantic Web is defined by Berners-Lee et al.
    [Show full text]
  • Linked Data Triples Enhance Document Relevance Classification
    applied sciences Article Linked Data Triples Enhance Document Relevance Classification Dinesh Nagumothu * , Peter W. Eklund , Bahadorreza Ofoghi and Mohamed Reda Bouadjenek School of Information Technology, Deakin University, Geelong, VIC 3220, Australia; [email protected] (P.W.E.); [email protected] (B.O.); [email protected] (M.R.B.) * Correspondence: [email protected] Abstract: Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is “about”, rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge-base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized news- feed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments Citation: Nagumothu, D.; Eklund, indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% P.W.; Ofoghi, B.; Bouadjenek, M.R.
    [Show full text]
  • Utilising Knowledge Graph Embeddings for Data-To-Text Generation
    Utilising Knowledge Graph Embeddings for Data-to-Text Generation Nivranshu Pasricha1, Mihael Arcan2 and Paul Buitelaar1,2 1SFI Centre for Research and Training in Artificial Intelligence 2 Insight SFI Research Centre for Data Analytics Data Science Institute, National University of Ireland Galway [email protected] Abstract In this work, we employ pre-trained knowledge Data-to-text generation has recently seen a graph embeddings (KGEs) for data-to-text gener- move away from modular and pipeline ar- ation with a model which is trained in an end-to- chitectures towards end-to-end architectures end fashion using an encoder-decoder style neu- based on neural networks. In this work, we ral network architecture. These embeddings have employ knowledge graph embeddings and ex- been shown to be useful in similar end-to-end archi- plore their utility for end-to-end approaches tectures especially in domain-specific and under- in a data-to-text generation task. Our experi- resourced scenarios for machine translation (Mous- ments show that using knowledge graph em- sallem et al., 2019). We focus on the WebNLG cor- beddings can yield an improvement of up to 2 – 3 BLEU points for seen categories on the pus which contains RDF-triples paired with verbal- WebNLG corpus without modifying the under- isations in English. We compare the use of KGEs lying neural network architecture. to two baseline models – the standard sequence- to-sequence model with attention (Bahdanau et al., 1 Introduction 2015) and the transformer model (Vaswani et al., Data-to-text generation is concerned with building 2017). We also do a comparison with pre-trained systems that can produce meaningful texts in a hu- GloVe word-embeddings (Pennington et al., 2014).
    [Show full text]
  • Learning Effective Embeddings from Medical Notes
    Learning Effective Embeddings from Medical Notes Sebastien Dubois Nathanael Romano Center for Biomedical Informatics Research Center for Biomedical Informatics Research Stanford University Stanford University Stanford, CA Stanford, CA [email protected] [email protected] Abstract With the large amount of available data and the variety of features they offer, electronic health records (EHR) have gotten a lot of interest over recent years, and start to be widely used by the machine learning and bioinformatics communities. While typical numerical fields such as demographics, vitals, lab measure- ments, diagnoses and procedures, are natural to use in machine learning models, there is no consensus yet on how to use the free-text clinical notes. We show how embeddings can be learned from patients’ history of notes, at the word, note and patient level, using simple neural and sequence models. We show on vari- ous relevant evaluation tasks that these embeddings are easily transferable to smaller problems, where they enable accurate predictions using only clinical notes. 1 Introduction With the rise of electronic health records (EHR), applying machine learning methods to patient data has raised a lot of attention over the past years, for problems such as survival analysis, causal effect inference, mortality prediction, etc. However, whereas the EHR data warehouses are usually enormous, a common setback often met by bioinformatics researchers is that populations quickly become scarce when restricted to cohorts of interest, rendering almost impossible using the raw high-dimensional data. To that extent, several attempts have been made in learning general-purpose representations from patient records, to make downstream predictions easier and more accurate.
    [Show full text]
  • Investigating the Impact of Pre-Trained Word Embeddings on Memorization in Neural Networks Aleena Thomas, David Adelani, Ali Davody, Aditya Mogadala, Dietrich Klakow
    Investigating the Impact of Pre-trained Word Embeddings on Memorization in Neural Networks Aleena Thomas, David Adelani, Ali Davody, Aditya Mogadala, Dietrich Klakow To cite this version: Aleena Thomas, David Adelani, Ali Davody, Aditya Mogadala, Dietrich Klakow. Investigating the Impact of Pre-trained Word Embeddings on Memorization in Neural Networks. 23rd International Conference on Text, Speech and Dialogue, Sep 2020, brno, Czech Republic. hal-02880590 HAL Id: hal-02880590 https://hal.inria.fr/hal-02880590 Submitted on 25 Jun 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Investigating the Impact of Pre-trained Word Embeddings on Memorization in Neural Networks Aleena Thomas[0000−0003−3606−8405], David Ifeoluwa Adelani, Ali Davody, Aditya Mogadala, and Dietrich Klakow Spoken Language Systems Group Saarland Informatics Campus, Saarland University, Saarbr¨ucken, Germany fathomas, didelani, adavody, amogadala, [email protected] Abstract. The sensitive information present in the training data, poses a privacy concern for applications as their unintended memorization dur- ing training can make models susceptible to membership inference and attribute inference attacks. In this paper, we investigate this problem in various pre-trained word embeddings (GloVe, ELMo and BERT) with the help of language models built on top of it.
    [Show full text]