Knowledge-Powered Deep Learning for Word Embedding
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Arxiv:2002.06235V1
Semantic Relatedness and Taxonomic Word Embeddings Magdalena Kacmajor John D. Kelleher Innovation Exchange ADAPT Research Centre IBM Ireland Technological University Dublin [email protected] [email protected] Filip Klubickaˇ Alfredo Maldonado ADAPT Research Centre Underwriters Laboratories Technological University Dublin [email protected] [email protected] Abstract This paper1 connects a series of papers dealing with taxonomic word embeddings. It begins by noting that there are different types of semantic relatedness and that different lexical representa- tions encode different forms of relatedness. A particularly important distinction within semantic relatedness is that of thematic versus taxonomic relatedness. Next, we present a number of ex- periments that analyse taxonomic embeddings that have been trained on a synthetic corpus that has been generated via a random walk over a taxonomy. These experiments demonstrate how the properties of the synthetic corpus, such as the percentage of rare words, are affected by the shape of the knowledge graph the corpus is generated from. Finally, we explore the interactions between the relative sizes of natural and synthetic corpora on the performance of embeddings when taxonomic and thematic embeddings are combined. 1 Introduction Deep learning has revolutionised natural language processing over the last decade. A key enabler of deep learning for natural language processing has been the development of word embeddings. One reason for this is that deep learning intrinsically involves the use of neural network models and these models only work with numeric inputs. Consequently, applying deep learning to natural language processing first requires developing a numeric representation for words. Word embeddings provide a way of creating numeric representations that have proven to have a number of advantages over traditional numeric rep- resentations of language. -
Text Mining Course for KNIME Analytics Platform
Text Mining Course for KNIME Analytics Platform KNIME AG Copyright © 2018 KNIME AG Table of Contents 1. The Open Analytics Platform 2. The Text Processing Extension 3. Importing Text 4. Enrichment 5. Preprocessing 6. Transformation 7. Classification 8. Visualization 9. Clustering 10. Supplementary Workflows Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 2 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ Overview KNIME Analytics Platform Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 3 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ What is KNIME Analytics Platform? • A tool for data analysis, manipulation, visualization, and reporting • Based on the graphical programming paradigm • Provides a diverse array of extensions: • Text Mining • Network Mining • Cheminformatics • Many integrations, such as Java, R, Python, Weka, H2O, etc. Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 4 Noncommercial-Share Alike license 2 https://creativecommons.org/licenses/by-nc-sa/4.0/ Visual KNIME Workflows NODES perform tasks on data Not Configured Configured Outputs Inputs Executed Status Error Nodes are combined to create WORKFLOWS Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 5 Noncommercial-Share Alike license 3 https://creativecommons.org/licenses/by-nc-sa/4.0/ Data Access • Databases • MySQL, MS SQL Server, PostgreSQL • any JDBC (Oracle, DB2, …) • Files • CSV, txt -
Combining Word Embeddings with Semantic Resources
AutoExtend: Combining Word Embeddings with Semantic Resources Sascha Rothe∗ LMU Munich Hinrich Schutze¨ ∗ LMU Munich We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The obtained embeddings live in the same vector space as the input word embeddings. A sparse tensor formalization guar- antees efficiency and parallelizability. We use WordNet, GermaNet, and Freebase as semantic resources. AutoExtend achieves state-of-the-art performance on Word-in-Context Similarity and Word Sense Disambiguation tasks. 1. Introduction Unsupervised methods for learning word embeddings are widely used in natural lan- guage processing (NLP). The only data these methods need as input are very large corpora. However, in addition to corpora, there are many other resources that are undoubtedly useful in NLP, including lexical resources like WordNet and Wiktionary and knowledge bases like Wikipedia and Freebase. We will simply refer to these as resources. In this article, we present AutoExtend, a method for enriching these valuable resources with embeddings for non-word objects they describe; for example, Auto- Extend enriches WordNet with embeddings for synsets. The word embeddings and the new non-word embeddings live in the same vector space. Many NLP applications benefit if non-word objects described by resources—such as synsets in WordNet—are also available as embeddings. -
Application of Machine Learning in Automatic Sentiment Recognition from Human Speech
Application of Machine Learning in Automatic Sentiment Recognition from Human Speech Zhang Liu Ng EYK Anglo-Chinese Junior College College of Engineering Singapore Nanyang Technological University (NTU) Singapore [email protected] Abstract— Opinions and sentiments are central to almost all human human activities and have a wide range of applications. As activities and have a wide range of applications. As many decision many decision makers turn to social media due to large makers turn to social media due to large volume of opinion data volume of opinion data available, efficient and accurate available, efficient and accurate sentiment analysis is necessary to sentiment analysis is necessary to extract those data. Business extract those data. Hence, text sentiment analysis has recently organisations in different sectors use social media to find out become a popular field and has attracted many researchers. However, consumer opinions to improve their products and services. extracting sentiments from audio speech remains a challenge. This project explores the possibility of applying supervised Machine Political party leaders need to know the current public Learning in recognising sentiments in English utterances on a sentiment to come up with campaign strategies. Government sentence level. In addition, the project also aims to examine the effect agencies also monitor citizens’ opinions on social media. of combining acoustic and linguistic features on classification Police agencies, for example, detect criminal intents and cyber accuracy. Six audio tracks are randomly selected to be training data threats by analysing sentiment valence in social media posts. from 40 YouTube videos (monologue) with strong presence of In addition, sentiment information can be used to make sentiments. -
Word-Like Character N-Gram Embedding
Word-like character n-gram embedding Geewook Kim and Kazuki Fukui and Hidetoshi Shimodaira Department of Systems Science, Graduate School of Informatics, Kyoto University Mathematical Statistics Team, RIKEN Center for Advanced Intelligence Project fgeewook, [email protected], [email protected] Abstract Table 1: Top-10 2-grams in Sina Weibo and 4-grams in We propose a new word embedding method Japanese Twitter (Experiment 1). Words are indicated called word-like character n-gram embed- by boldface and space characters are marked by . ding, which learns distributed representations FNE WNE (Proposed) Chinese Japanese Chinese Japanese of words by embedding word-like character n- 1 ][ wwww 自己 フォロー grams. Our method is an extension of recently 2 。␣ !!!! 。␣ ありがと proposed segmentation-free word embedding, 3 !␣ ありがと ][ wwww 4 .. りがとう 一个 !!!! which directly embeds frequent character n- 5 ]␣ ございま 微博 めっちゃ grams from a raw corpus. However, its n-gram 6 。。 うござい 什么 んだけど vocabulary tends to contain too many non- 7 ,我 とうござ 可以 うござい 8 !! ざいます 没有 line word n-grams. We solved this problem by in- 9 ␣我 がとうご 吗? 2018 troducing an idea of expected word frequency. 10 了, ください 哈哈 じゃない Compared to the previously proposed meth- ods, our method can embed more words, along tion tools are used to determine word boundaries with the words that are not included in a given in the raw corpus. However, these segmenters re- basic word dictionary. Since our method does quire rich dictionaries for accurate segmentation, not rely on word segmentation with rich word which are expensive to prepare and not always dictionaries, it is especially effective when the available. -
Text KM & Text Mining
Text KM & Text Mining An Introduction to Managing Knowledge in Unstructured Natural Language Documents Dekai Wu HKUST Human Language Technology Center Department of Computer Science and Engineering University of Science and Technology Hong Kong http://www.cs.ust.hk/~dekai © 2008 Dekai Wu Lecture Objectives Introduction to the concept of Text KM and Text Mining (TM) How to exploit knowledge encoded in text form How text mining is different from data mining Introduction to the various aspects of Natural Language Processing (NLP) Introduction to the different tools and methods available for TM HKUST Human Language Technology Center © 2008 Dekai Wu Textual Knowledge Management Text KM oversees the storage, capturing and sharing of knowledge encoded in unstructured natural language documents 80-90% of an organization’s explicit knowledge resides in plain English (Chinese, Japanese, Spanish, …) documents – not in structured relational databases! Case libraries are much more reasonably stored as natural language documents, than encoded into relational databases Most knowledge encoded as text will never pass through explicit KM processes (eg, email) HKUST Human Language Technology Center © 2008 Dekai Wu Text Mining Text Mining analyzes unstructured natural language documents to extract targeted types of knowledge Extracts knowledge that can then be inserted into databases, thereby facilitating structured data mining techniques Provides a more natural user interface for entering knowledge, for both employees and developers Reduces -
Learned in Speech Recognition: Contextual Acoustic Word Embeddings
LEARNED IN SPEECH RECOGNITION: CONTEXTUAL ACOUSTIC WORD EMBEDDINGS Shruti Palaskar∗, Vikas Raunak∗ and Florian Metze Carnegie Mellon University, Pittsburgh, PA, U.S.A. fspalaska j vraunak j fmetze [email protected] ABSTRACT model [10, 11, 12] trained for direct Acoustic-to-Word (A2W) speech recognition [13]. Using this model, we jointly learn to End-to-end acoustic-to-word speech recognition models have re- automatically segment and classify input speech into individual cently gained popularity because they are easy to train, scale well to words, hence getting rid of the problem of chunking or requiring large amounts of training data, and do not require a lexicon. In addi- pre-defined word boundaries. As our A2W model is trained at the tion, word models may also be easier to integrate with downstream utterance level, we show that we can not only learn acoustic word tasks such as spoken language understanding, because inference embeddings, but also learn them in the proper context of their con- (search) is much simplified compared to phoneme, character or any taining sentence. We also evaluate our contextual acoustic word other sort of sub-word units. In this paper, we describe methods embeddings on a spoken language understanding task, demonstrat- to construct contextual acoustic word embeddings directly from a ing that they can be useful in non-transcription downstream tasks. supervised sequence-to-sequence acoustic-to-word speech recog- Our main contributions in this paper are the following: nition model using the learned attention distribution. On a suite 1. We demonstrate the usability of attention not only for aligning of 16 standard sentence evaluation tasks, our embeddings show words to acoustic frames without any forced alignment but also for competitive performance against a word2vec model trained on the constructing Contextual Acoustic Word Embeddings (CAWE). -
Recurrent Neural Network Based Language Model
INTERSPEECH 2010 Recurrent neural network based language model Toma´sˇ Mikolov1;2, Martin Karafiat´ 1, Luka´sˇ Burget1, Jan “Honza” Cernockˇ y´1, Sanjeev Khudanpur2 1Speech@FIT, Brno University of Technology, Czech Republic 2 Department of Electrical and Computer Engineering, Johns Hopkins University, USA fimikolov,karafiat,burget,[email protected], [email protected] Abstract INPUT(t) OUTPUT(t) A new recurrent neural network based language model (RNN CONTEXT(t) LM) with applications to speech recognition is presented. Re- sults indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empiri- cal evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high com- putational (training) complexity. Index Terms: language modeling, recurrent neural networks, speech recognition CONTEXT(t-1) 1. Introduction Figure 1: Simple recurrent neural network. Sequential data prediction is considered by many as a key prob- lem in machine learning and artificial intelligence (see for ex- ample [1]). The goal of statistical language modeling is to plex and often work well only for systems based on very limited predict the next word in textual data given context; thus we amounts of training data. -
Local Homology of Word Embeddings
Local Homology of Word Embeddings Tadas Temcinasˇ 1 Abstract Intuitively, stratification is a decomposition of a topologi- Topological data analysis (TDA) has been widely cal space into manifold-like pieces. When thinking about used to make progress on a number of problems. stratification learning and word embeddings, it seems intu- However, it seems that TDA application in natural itive that vectors of words corresponding to the same broad language processing (NLP) is at its infancy. In topic would constitute a structure, which we might hope this paper we try to bridge the gap by arguing why to be a manifold. Hence, for example, by looking at the TDA tools are a natural choice when it comes to intersections between those manifolds or singularities on analysing word embedding data. We describe the manifolds (both of which can be recovered using local a parallelisable unsupervised learning algorithm homology-based algorithms (Nanda, 2017)) one might hope based on local homology of datapoints and show to find vectors of homonyms like ‘bank’ (which can mean some experimental results on word embedding either a river bank, or a financial institution). This, in turn, data. We see that local homology of datapoints in has potential to help solve the word sense disambiguation word embedding data contains some information (WSD) problem in NLP, which is pinning down a particular that can potentially be used to solve the word meaning of a word used in a sentence when the word has sense disambiguation problem. multiple meanings. In this work we present a clustering algorithm based on lo- cal homology, which is more relaxed1 that the stratification- 1. -
Word Embedding, Sense Embedding and Their Application to Word Sense Induction
Word Embeddings, Sense Embeddings and their Application to Word Sense Induction Linfeng Song The University of Rochester Computer Science Department Rochester, NY 14627 Area Paper April 2016 Abstract This paper investigates the cutting-edge techniques for word embedding, sense embedding, and our evaluation results on large-scale datasets. Word embedding refers to a kind of methods that learn a distributed dense vector for each word in a vocabulary. Traditional word embedding methods first obtain the co-occurrence matrix then perform dimension reduction with PCA. Recent methods use neural language models that directly learn word vectors by predicting the context words of the target word. Moving one step forward, sense embedding learns a distributed vector for each sense of a word. They either define a sense as a cluster of contexts where the target word appears or define a sense based on a sense inventory. To evaluate the performance of the state-of-the-art sense embedding methods, I first compare them on the dominant word similarity datasets, then compare them on my experimental settings. In addition, I show that sense embedding is applicable to the task of word sense induction (WSI). Actually we are the first to show that sense embedding methods are competitive on WSI by building sense-embedding-based systems that demonstrate highly competitive performances on the SemEval 2010 WSI shared task. Finally, I propose several possible future research directions on word embedding and sense embedding. The University of Rochester Computer Science Department supported this work. Contents 1 Introduction 3 2 Word Embedding 5 2.1 Skip-gramModel................................ -
Unified Language Model Pre-Training for Natural
Unified Language Model Pre-training for Natural Language Understanding and Generation Li Dong∗ Nan Yang∗ Wenhui Wang∗ Furu Wei∗† Xiaodong Liu Yu Wang Jianfeng Gao Ming Zhou Hsiao-Wuen Hon Microsoft Research {lidong1,nanya,wenwan,fuwei}@microsoft.com {xiaodl,yuwan,jfgao,mingzhou,hon}@microsoft.com Abstract This paper presents a new UNIfied pre-trained Language Model (UNILM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirec- tional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UNILM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UNILM achieves new state-of- the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at https://github.com/microsoft/unilm. 1 Introduction Language model (LM) pre-training has substantially advanced the state of the art across a variety of natural language processing tasks [8, 29, 19, 31, 9, 1]. -
Hello, It's GPT-2
Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems Paweł Budzianowski1;2;3 and Ivan Vulic´2;3 1Engineering Department, Cambridge University, UK 2Language Technology Lab, Cambridge University, UK 3PolyAI Limited, London, UK [email protected], [email protected] Abstract (Young et al., 2013). On the other hand, open- domain conversational bots (Li et al., 2017; Serban Data scarcity is a long-standing and crucial et al., 2017) can leverage large amounts of freely challenge that hinders quick development of available unannotated data (Ritter et al., 2010; task-oriented dialogue systems across multiple domains: task-oriented dialogue models are Henderson et al., 2019a). Large corpora allow expected to learn grammar, syntax, dialogue for training end-to-end neural models, which typ- reasoning, decision making, and language gen- ically rely on sequence-to-sequence architectures eration from absurdly small amounts of task- (Sutskever et al., 2014). Although highly data- specific data. In this paper, we demonstrate driven, such systems are prone to producing unre- that recent progress in language modeling pre- liable and meaningless responses, which impedes training and transfer learning shows promise their deployment in the actual conversational ap- to overcome this problem. We propose a task- oriented dialogue model that operates solely plications (Li et al., 2017). on text input: it effectively bypasses ex- Due to the unresolved issues with the end-to- plicit policy and language generation modules. end architectures, the focus has been extended to Building on top of the TransferTransfo frame- retrieval-based models.