Distributed Representation of Sentences
|
|
- Kerrie Blankenship
- 6 years ago
- Views:
Transcription
1 Distributed Representation of Sentences LU Yangyang July KERE Seminar
2 Outline Distributed Representation of Sentences and Documents. ICML 14 Word Vector Paragraph Vector Experiments of NLP Tasks A Convolutional Neural Network for Modelling Sentences. ACL 14 DCNN: Convolutional Neural Networks Experiments of NLP Tasks Multilingual Models for Compositional Distributed Semantics. ACL 14 Composition Models Experiments Summary
3 Authors Distributed Representation of Sentences and Documents. ICML 14 1 Quoc Le, Tomas Mikolov Google Inc, Mountain View A Convolutional Neural Network for Modelling Sentences. ACL 14 2 Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom University of Oxford Multilingual Models for Compositional Distributed Semantics. ACL 14 Karl Moritz Hermann, Phil Blunsom University of Oxford
4 Outline Distributed Representation of Sentences and Documents. ICML 14 Word Vector Paragraph Vector Experiments of NLP Tasks A Convolutional Neural Network for Modelling Sentences. ACL 14 Multilingual Models for Compositional Distributed Semantics. ACL 14 Summary
5 Outline Distributed Representation of Sentences and Documents. ICML 14 Word Vector Paragraph Vector Experiments of NLP Tasks A Convolutional Neural Network for Modelling Sentences. ACL 14 DCNN: Convolutional Neural Networks Experiments of NLP Tasks Multilingual Models for Compositional Distributed Semantics. ACL 14 Composition Models Experiments Summary
6 Recall: Word Vector 3 Every word: A unique vector, represented by a column in a matrix W Given a sequence of training words w 1, w 2, w 3,..., w T : 3 Mikolov T, et al. Efficient estimation of word representations in vector space[c]. ICLR workshop, 2013
7 Recall: Word Vector 3 Every word: A unique vector, represented by a column in a matrix W Given a sequence of training words w 1, w 2, w 3,..., w T : Predicting a word given the other words in a context (CBOW) Predicting the surrounding words given a word (Skip-gram) 3 Mikolov T, et al. Efficient estimation of word representations in vector space[c]. ICLR workshop, 2013
8 Recall: Word Vector The Skip-gram Model 4 Predicting the surrounding words given a word in sentence The objective: maximize where 1 T T t=1 c j c,j 0 log p(w t+j w t ) c : the size of the training context 4 Mikolov T, et al. Distributed representations of words and phrases and their compositionality[j]. Advances in Neural Information Processing Systems, 2013
9 Recall: Word Vector Continuous Bag-of-Words Model(CBOW) 5 Predicting a word given the other words in a context The projection layer: shared for all words (not just the projection matrix) The objective: maximize T 1 k log p(w t w t k,..., w t+k ) T t=k 5 Mikolov T, et al. Efficient estimation of word representations in vector space[c]. ICLR workshop, 2013
10 Word Vector The objective: maximize 1 T T k t=k log p(w t w t k,..., w t+k ) The prediction task: via a multiple classifier (e.g. softmax 6 ) p(w t w t k,..., w t+k ) = eyw i eyi y = b + Uh(w t k,..., w t+k ; W ) where U, b : the softmax parameters h : a concatenation or average of word vectors extracted from W 6 GOTO 53
11 Outline Distributed Representation of Sentences and Documents. ICML 14 Word Vector Paragraph Vector Experiments of NLP Tasks A Convolutional Neural Network for Modelling Sentences. ACL 14 DCNN: Convolutional Neural Networks Experiments of NLP Tasks Multilingual Models for Compositional Distributed Semantics. ACL 14 Composition Models Experiments Summary
12 Paragraph Vector PV-DM: A Distributed Memory Model The paragraph vectors are asked to contribute to the prediction task of the next word given many contexts sampled from the paragraph. The paragraph acts as a memory that remembers what is missing from the current context or the topic of the paragraph.
13 PV-DM Every paragraph: a column in matrix D Shared across all contexts generated from the same paragraph but not across paragraphs Every word: a column in matrix W Shared across paragraphs Sampled from a fixed-length context over the paragraph Concatenate paragraph and word vectors
14 PV-DM Every paragraph: a column in matrix D Shared across all contexts generated from the same paragraph but not across paragraphs Every word: a column in matrix W Shared across paragraphs Sampled from a fixed-length context over the paragraph Concatenate paragraph and word vectors The only change compared to the word vector model: y = b + Uh(w t k,..., w t+k, d; W, D) where h : constructed from W and D d : the vector of the paragraph from which the context is sampled
15 Paragraph Vector without word ordering PV-DBOW: Distributed Bag-Of-Words 7 Ignore the context words in the input Force the model to predict words randomly sampled from the paragraph in the output Sample a text window Sample a random word from the text window Form a classification task given the Paragraph Vector 7 Skip-gram Model: GOTO 7
16 Outline Distributed Representation of Sentences and Documents. ICML 14 Word Vector Paragraph Vector Experiments of NLP Tasks A Convolutional Neural Network for Modelling Sentences. ACL 14 DCNN: Convolutional Neural Networks Experiments of NLP Tasks Multilingual Models for Compositional Distributed Semantics. ACL 14 Composition Models Experiments Summary
17 Dataset: Sentiment Analysis Stanford Sentiment Treebank Dataset sentences taken from the movie review site Rotten Tomatoes train/test/development: 8544/2210/1101 sentences sentence/subphrase labels: 5-way fine-grained(+ + / + /0/ / ), binary coarse-grained(pos/neg) here only consider labeling the full sentences treat a sentence as a paragraph 8 Socher, R. et al. Recursive deep models for semantic compositionality over a sentiment treebank. EMNLP, 2013
18 Dataset: Sentiment Analysis Stanford Sentiment Treebank Dataset sentences taken from the movie review site Rotten Tomatoes train/test/development: 8544/2210/1101 sentences sentence/subphrase labels: 5-way fine-grained(+ + / + /0/ / ), binary coarse-grained(pos/neg) here only consider labeling the full sentences treat a sentence as a paragraph Experiment protocols: Paraphrase Vector: a concatenation of PV-DM and PV-DBOW PV-DM: 400 dimensions, PV-DBOW: 400 dimensions The optimal window size: 8 Predictor of the movie rating: a logistic regression 8 Socher, R. et al. Recursive deep models for semantic compositionality over a sentiment treebank. EMNLP, 2013
19 Dataset: Sentiment Analysis IMDB Dataset 9 100, 000 movie reviews taken from IMDB each movie review: several sentences labeled train/unlabeled train/labeled test: 25, 000/50, 000/25, 000 labels: binary (pos/neg) 9 Maas, et al. Learning word vectors for sentiment analysis. ACL, 2011
20 Dataset: Sentiment Analysis IMDB Dataset 9 100, 000 movie reviews taken from IMDB each movie review: several sentences labeled train/unlabeled train/labeled test: 25, 000/50, 000/25, 000 labels: binary (pos/neg) Experimental protocols: PV-DM: 400 dimensions, PV-DBOW: 400 dimensions Learning word vectors and paragraph vectors: 25, 000 labeled + 50, 000 unlabeled The predictor: a neural network with one hidden layer with 50 units and a logistic classifier The optimal window size: 10 9 Maas, et al. Learning word vectors for sentiment analysis. ACL, 2011
21 Sentiment Analysis (cont.)
22 Information Retrieval with Paragraph Vector Dataset: 1, 000, 000 most popular queries top 10 results, by a search engine Constructing a triplet of paragraphs: 1 st, 2 nd : results of the same query 3 rd : randomly sampled from the rest collection (different query) Task: identify which of the triplet are the results of the same query
23 Outline Distributed Representation of Sentences and Documents. ICML 14 A Convolutional Neural Network for Modelling Sentences. ACL 14 DCNN: Convolutional Neural Networks Experiments of NLP Tasks Multilingual Models for Compositional Distributed Semantics. ACL 14 Summary
24 Outline Distributed Representation of Sentences and Documents. ICML 14 Word Vector Paragraph Vector Experiments of NLP Tasks A Convolutional Neural Network for Modelling Sentences. ACL 14 DCNN: Convolutional Neural Networks Experiments of NLP Tasks Multilingual Models for Compositional Distributed Semantics. ACL 14 Composition Models Experiments Summary
25 Recall: Max-TDNN Sentence Model 10 TDNNs: Time-Delay Neural Networks Modeling long-distance dependencies time refers to the idea that a sequence has a notion of order. A TDNN reads the sequence in an online fashion: at time t 1, one sees x t, the t-th word in the sentence. A classical TDNN layer: A convolution on a given sequence x( ) Outputting another sequence o( ) 10 Ronan Collobert and Jason Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. ICML, 2008
26 DCNN: Overview Convolutional Neural Networks with Dynamic k-max Pooling
27 Wide Convolution Each word w i R d Sentence matrix s R d s Weight matrix for convolving m R d m Matrix after convolution c R d (s+m 1)
28 (Dynamic) k-max Pooling k-max Pooling: A generalisation of the max pooling over the time dimension 11 Different from the local max pooling operations Max-TDNN: Ronan Collobert and Jason Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. ICML,
29 (Dynamic) k-max Pooling k-max Pooling: A generalisation of the max pooling over the time dimension 11 Different from the local max pooling operations 12 Given a value k and a sequence p R p (p k), k-max pooling selects the subsequence p k max of the k highest values of p. The order of the values in p k max max corresponds to their original order in p. 11 Max-TDNN: Ronan Collobert and Jason Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. ICML,
30 (Dynamic) k-max Pooling k-max Pooling: A generalisation of the max pooling over the time dimension 11 Different from the local max pooling operations 12 Given a value k and a sequence p R p (p k), k-max pooling selects the subsequence p k max of the k highest values of p. The order of the values in p k max max corresponds to their original order in p. Dynamic k-max Pooling: k l = max(k top, where L l L s) l : the number of the current convolutional layer to which the pooling is applied L : the total number of convolutional layers in the network k top : the fixed pooling parameter for the topmost convolutional layer 11 Max-TDNN: Ronan Collobert and Jason Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. ICML,
31 Non-linear Feature Function Apply the convolution + non-linear layers 13 each d-dimension column a in the matrix a: M = [diag(m :,1 ),..., diag(m :,m)] where m : the weights of the d filters of the wide convolution A wide convolution + a (dynamic) k-max pooling layer + a non-linear function + the input sentence matrix a first order feature map 13 Temporarily ignore the pooling layer
32 Multiple Feature Maps Repeating: wide convolution + (dynamic) k-max pooling + non-linear function feature maps of increasing order 14 F i j = n k=1 m i j,k * F i 1 k where F i j : the j-th feature map of the i-th order * : wide convolution m i j,k : convolving matrix( all the m i j,k form an order-4 tensor) 14 LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series[j]. The handbook of brain theory and neural networks, 1995
33 Folding In the formulation of the network so far: Applying feature detectors to an individual row Creating complex dependencies across the same rows in multiple feature maps Feature detectors in different rows, however, are independent of each other until the top fully connected layer. Folding: For a map of d rows, folding returns a map of d/2 rows Halving the size of the representation With a folding layer, a feature detector of the i-th order depends now on two rows of feature values in the lower maps of order i?1
34 Outline Distributed Representation of Sentences and Documents. ICML 14 Word Vector Paragraph Vector Experiments of NLP Tasks A Convolutional Neural Network for Modelling Sentences. ACL 14 DCNN: Convolutional Neural Networks Experiments of NLP Tasks Multilingual Models for Compositional Distributed Semantics. ACL 14 Composition Models Experiments Summary
35 Training The top layer of the network has a fully connected layer followed by a softmax non-linearity The softmax layer: predicts the probability distribution over classes given the input sentence The objective: To minimise the cross-entropy of the predicted and true distributions Including an L 2 regularization term
36 Sentiment Prediction in Movie Reviews Stanford Sentiment Treebank Dataset
37 Question Type Classification Six different question types Train/test: 5452/500 TREC Dataset DCNN: word dimension d = 32, a single convolution layer with filters of size 8 and 5 feature maps
38 Twitter Sentiment Prediction with Distant Supervision A tweet is automatically labelled as positive or negative depending on the emotion that occurs in it Train/test: 1.6 million(emotion-based labels)/400 (hand-annotated labels) Preprocessing: a vocabulary of word types DCNN: word dimension d = 60, other parameters same as the binary sentiment prediction task of Stanford Sentiment Treebank
39 Visualising Feature Detectors A filter in the DCNN: associated with a feature detector or neuron that learns during training to be particularly active when presented with a specific sequence of input words The first layer: continuous n-grams Higher layers: multiple separate n-grams
40 Outline Distributed Representation of Sentences and Documents. ICML 14 A Convolutional Neural Network for Modelling Sentences. ACL 14 Multilingual Models for Compositional Distributed Semantics. ACL 14 Composition Models Experiments Summary
41 Multilingual Models for Compositional Distributed Semantics Representing meaning across languages in a shared multilingual semantic space Proposing a novel unsupervised technique leverages parallel corpora employs semantic transfer through compositional representations Experiments on two corpora: cross-lingual document classification on the Reuters RCV1/RCV2 corpora classification on a massively multilingual corpus which we derive from the TED corpus
42 Outline Distributed Representation of Sentences and Documents. ICML 14 Word Vector Paragraph Vector Experiments of NLP Tasks A Convolutional Neural Network for Modelling Sentences. ACL 14 DCNN: Convolutional Neural Networks Experiments of NLP Tasks Multilingual Models for Compositional Distributed Semantics. ACL 14 Composition Models Experiments Summary
43 Overview Word representation: a continuous vector in R d Semantic representations of sentence and document: Computed by a compositional vector model(cvm) A multilingual objective function: Using a noise-contrastive update between semantic representations of different languages to learn these word embeddings (a) The cat sat on the red mat. (b) 猫坐在红色的垫子上 (a) The cat sat on the red mat. (b) Die Katze saß auf der roten Matte.
44 Approach Given enough parallel data, a shared representation of two parallel sentences would be forced to capture the common elements between these two sentences. What parallel sentences share, of course, are their semantics.
45 Approach Given enough parallel data, a shared representation of two parallel sentences would be forced to capture the common elements between these two sentences. What parallel sentences share, of course, are their semantics. Define a bilingual energy: where E bi (a, b) = f(a) g(b) 2 C : a parallel corpus x, y : two different languages (a, b) C : two sentences of languages x, y f : X R d g : Y R d
46 Approach (cont.) The objective: minimize E bi for all semantically equivalent sentences in the corpus E hl (a, b, n) = [m + E bi (a, b) E bi (a, n)] + where [x] + = max(x, 0) (a, b) C : positive sample (a, n) C : negative(or noise) sample
47 Approach (cont.) The objective: minimize E bi for all semantically equivalent sentences in the corpus E hl (a, b, n) = [m + E bi (a, b) E bi (a, n)] + where [x] + = max(x, 0) (a, b) C : positive sample (a, n) C : negative(or noise) sample The final objective function: minimize J(θ) = k E hl (a, b, n i ) + λ 2 θ 2 (a,b) C i=1 where θ : all the parameters in the model
48 Composition Models: CVM Focus on composition functions that do not require any syntactic information ADD model: f(x) = n i x i A sentence is represented by the sum of its word vectors A distributed bag-of-words approach: ignore the sentence order
49 Composition Models: CVM Focus on composition functions that do not require any syntactic information ADD model: f(x) = n i x i A sentence is represented by the sum of its word vectors A distributed bag-of-words approach: ignore the sentence order BI model: Capture bi-gram information A non-linear function f(x) = n i tanh(x i 1 + x i )
50 Document-level Semantics For a number of tasks, such as topic modelling, representations of objects beyond the sentence level are required. Extend model to document-level learning: recursively applying the composition and objective function
51 Outline Distributed Representation of Sentences and Documents. ICML 14 Word Vector Paragraph Vector Experiments of NLP Tasks A Convolutional Neural Network for Modelling Sentences. ACL 14 DCNN: Convolutional Neural Networks Experiments of NLP Tasks Multilingual Models for Compositional Distributed Semantics. ACL 14 Composition Models Experiments Summary
52 Dataset: The Europarl corpus v7(rcv) 15 Experiment settings used for the Cross-Lingual Document Classification(CLDC) task considered the English-German and English-French language pairs A massively multilingual corpus based on the TED corpus 16 for IWSLT 2013 training: 12, 078 parallel documents (12 languages) used for the topic classification task: 15 most frequent keywords as topics Experiment protocols: All model weights were randomly initialised using a Gaussian distribution (μ = 0, σ 2 = 0.1). The number of noise samples for each positive samples: {1, 10, 50} The dimension of all embeddings: d = 128 Iterations: 100 for RCV, 500 for TED, 5 for joint
53 RCV1/RCV2 Document Classification ADD: training on 500k sentence pairs of the English-German parallel section ADD+: using an additional 500k parallel sentences from the English-French corpus Training the document classifier: using varying sizes between 100 and 10, 000 documents
54 TED Corpus Experiments Using the training data of the corpus to learn distributed representations across 12 languages In the single mode:vectors are learnt from a single language pair (en-x) In the joint mode: vector learning is performed on all parallel sub-corpora simultaneously.
55 Linguistic Analysis
56 Outline Distributed Representation of Sentences and Documents. ICML 14 A Convolutional Neural Network for Modelling Sentences. ACL 14 Multilingual Models for Compositional Distributed Semantics. ACL 14 Summary
57 Summary Mikolov, ICML 14 An unsupervised learning of paragraph vector PV-DM PV-DBOW Learning to predict the surrounding words in contexts sampled from the paragraph Lossing the word order information NLP tasks: Sentiment prediction (Stanford, IMDB) Information retrieval (computing similarity between snippets)
58 Summary(cont.) Kalchbrenner,ACL 14 A dynamic convolutional neural network DCNN Wide convolution + folding + (dynamic) k-max pooling + non-linearity NLP tasks: Sentiment prediction (Stanford, Twitter) Question type classification Visualizing feature detectors
59 Hermann,ACL 14 Summary(cont.) A novel method for learning multilingual word embeddings Leveraging parallel data Defining a multilingual objective function Coupled with simple composition functions CVM & DocCVM: ADD, BI NLP tasks: Cross-lingual document classification (Reuter RCV1/RCV2) Topic classification (TED) ALL (Mikolov 14, Kalchbrenner 14, Hermann 14): Without requiring external features as provided by parsers or other resources
60 Related Neural Sentence Models Neural Bag-of-Words(NBoW) models Mikolov T. et al. Distributed Representations of Words and Phrases and their Compositionality. NIPS, 2013 Bengio Y. et al. A Neural Probabilistic Language Model. JMLR, 2006 Models that adopts a more general structure Socher R. et al. Recursive deep models for semantic compositionality over a sentiment treebank. EMNLP, 2013 Socher R. et al. Grounded Compositional Semantics for Finding and Describing Images with Sentences. TACL, 2013 Jordan B. Pollack. Recursive distributed representations. Artificial Intelligence, 1990 Models based on convolution and TDNN architeture Kalchbrenner N. and Blunsom P. Recurrent Convolutional Neural Networks for Discourse Compositionality. ACL, 2013 Collobert R. and Weston J. A unified architecture for natural language processing: Deep neural networks with multitask learning. ICML, 2008
61 Thank You for Listening! Q & A
62 A Neural Probabilistic Language Model y = b + W x + U tanh(d + Hx) x = (C(w t 1 ), C(w t 2 ),..., C(w t n+1 )) 17 Bengio Y. et al. A Neural Probabilistic Language Model. JMLR, Word Vector: GOTO 9
63 Stanford Sentiment Treebank Socher R. et al. Recursive deep models for semantic compositionality over a sentiment treebank. EMNLP, 2013
System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks
System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering
More informationUnsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model
Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Xinying Song, Xiaodong He, Jianfeng Gao, Li Deng Microsoft Research, One Microsoft Way, Redmond, WA 98052, U.S.A.
More informationSemi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.
Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link
More informationarxiv: v1 [cs.cl] 20 Jul 2015
How to Generate a Good Word Embedding? Siwei Lai, Kang Liu, Liheng Xu, Jun Zhao National Laboratory of Pattern Recognition (NLPR) Institute of Automation, Chinese Academy of Sciences, China {swlai, kliu,
More informationPython Machine Learning
Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled
More informationLearning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models
Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Stephan Gouws and GJ van Rooyen MIH Medialab, Stellenbosch University SOUTH AFRICA {stephan,gvrooyen}@ml.sun.ac.za
More informationAssignment 1: Predicting Amazon Review Ratings
Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for
More informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
More informationGenerative models and adversarial training
Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?
More informationSecond Exam: Natural Language Parsing with Neural Networks
Second Exam: Natural Language Parsing with Neural Networks James Cross May 21, 2015 Abstract With the advent of deep learning, there has been a recent resurgence of interest in the use of artificial neural
More informationarxiv: v2 [cs.cl] 26 Mar 2015
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks Rie Johnson RJ Research Consulting Tarrytown, NY, USA riejohnson@gmail.com Tong Zhang Baidu Inc., Beijing, China Rutgers
More informationA deep architecture for non-projective dependency parsing
Universidade de São Paulo Biblioteca Digital da Produção Intelectual - BDPI Departamento de Ciências de Computação - ICMC/SCC Comunicações em Eventos - ICMC/SCC 2015-06 A deep architecture for non-projective
More informationAsk Me Anything: Dynamic Memory Networks for Natural Language Processing
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing Ankit Kumar*, Ozan Irsoy*, Peter Ondruska*, Mohit Iyyer*, James Bradbury, Ishaan Gulrajani*, Victor Zhong*, Romain Paulus, Richard
More informationLecture 1: Machine Learning Basics
1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3
More informationarxiv: v2 [cs.ir] 22 Aug 2016
Exploring Deep Space: Learning Personalized Ranking in a Semantic Space arxiv:1608.00276v2 [cs.ir] 22 Aug 2016 ABSTRACT Jeroen B. P. Vuurens The Hague University of Applied Science Delft University of
More informationA Vector Space Approach for Aspect-Based Sentiment Analysis
A Vector Space Approach for Aspect-Based Sentiment Analysis by Abdulaziz Alghunaim B.S., Massachusetts Institute of Technology (2015) Submitted to the Department of Electrical Engineering and Computer
More informationOPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS
OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,
More informationarxiv: v4 [cs.cl] 28 Mar 2016
LSTM-BASED DEEP LEARNING MODELS FOR NON- FACTOID ANSWER SELECTION Ming Tan, Cicero dos Santos, Bing Xiang & Bowen Zhou IBM Watson Core Technologies Yorktown Heights, NY, USA {mingtan,cicerons,bingxia,zhou}@us.ibm.com
More informationQuickStroke: An Incremental On-line Chinese Handwriting Recognition System
QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents
More informationKnowledge Transfer in Deep Convolutional Neural Nets
Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract
More informationГлубокие рекуррентные нейронные сети для аспектно-ориентированного анализа тональности отзывов пользователей на различных языках
Глубокие рекуррентные нейронные сети для аспектно-ориентированного анализа тональности отзывов пользователей на различных языках Тарасов Д. С. (dtarasov3@gmail.com) Интернет-портал reviewdot.ru, Казань,
More informationTraining a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski
Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski Problem Statement and Background Given a collection of 8th grade science questions, possible answer
More informationCourse Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE
EE-589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:00-12:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers
More informationON THE USE OF WORD EMBEDDINGS ALONE TO
ON THE USE OF WORD EMBEDDINGS ALONE TO REPRESENT NATURAL LANGUAGE SEQUENCES Anonymous authors Paper under double-blind review ABSTRACT To construct representations for natural language sequences, information
More informationA New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation
A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick
More informationDeep Neural Network Language Models
Deep Neural Network Language Models Ebru Arısoy, Tara N. Sainath, Brian Kingsbury, Bhuvana Ramabhadran IBM T.J. Watson Research Center Yorktown Heights, NY, 10598, USA {earisoy, tsainath, bedk, bhuvana}@us.ibm.com
More informationA Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval Yelong Shen Microsoft Research Redmond, WA, USA yeshen@microsoft.com Xiaodong He Jianfeng Gao Li Deng Microsoft Research
More informationTwitter Sentiment Classification on Sanders Data using Hybrid Approach
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118-123 www.iosrjournals.org Twitter Sentiment Classification on Sanders
More informationSemantic and Context-aware Linguistic Model for Bias Detection
Semantic and Context-aware Linguistic Model for Bias Detection Sicong Kuang Brian D. Davison Lehigh University, Bethlehem PA sik211@lehigh.edu, davison@cse.lehigh.edu Abstract Prior work on bias detection
More informationA Case Study: News Classification Based on Term Frequency
A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center
More informationA Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention
A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention Damien Teney 1, Peter Anderson 2*, David Golub 4*, Po-Sen Huang 3, Lei Zhang 3, Xiaodong He 3, Anton van den Hengel 1 1
More informationNatural Language Processing. George Konidaris
Natural Language Processing George Konidaris gdk@cs.brown.edu Fall 2017 Natural Language Processing Understanding spoken/written sentences in a natural language. Major area of research in AI. Why? Humans
More informationWord Embedding Based Correlation Model for Question/Answer Matching
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) Word Embedding Based Correlation Model for Question/Answer Matching Yikang Shen, 1 Wenge Rong, 2 Nan Jiang, 2 Baolin
More informationChinese Language Parsing with Maximum-Entropy-Inspired Parser
Chinese Language Parsing with Maximum-Entropy-Inspired Parser Heng Lian Brown University Abstract The Chinese language has many special characteristics that make parsing difficult. The performance of state-of-the-art
More informationDetecting English-French Cognates Using Orthographic Edit Distance
Detecting English-French Cognates Using Orthographic Edit Distance Qiongkai Xu 1,2, Albert Chen 1, Chang i 1 1 The Australian National University, College of Engineering and Computer Science 2 National
More informationPOS tagging of Chinese Buddhist texts using Recurrent Neural Networks
POS tagging of Chinese Buddhist texts using Recurrent Neural Networks Longlu Qin Department of East Asian Languages and Cultures longlu@stanford.edu Abstract Chinese POS tagging, as one of the most important
More informationResidual Stacking of RNNs for Neural Machine Translation
Residual Stacking of RNNs for Neural Machine Translation Raphael Shu The University of Tokyo shu@nlab.ci.i.u-tokyo.ac.jp Akiva Miura Nara Institute of Science and Technology miura.akiba.lr9@is.naist.jp
More informationCS Machine Learning
CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationA study of speaker adaptation for DNN-based speech synthesis
A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu, Pawel Swietojanski, Christophe Veaux, Steve Renals, Simon King The Centre for Speech Technology Research (CSTR) University of Edinburgh,
More informationarxiv: v2 [cs.cv] 30 Mar 2017
Domain Adaptation for Visual Applications: A Comprehensive Survey Gabriela Csurka arxiv:1702.05374v2 [cs.cv] 30 Mar 2017 Abstract The aim of this paper 1 is to give an overview of domain adaptation and
More informationAttributed Social Network Embedding
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, MAY 2017 1 Attributed Social Network Embedding arxiv:1705.04969v1 [cs.si] 14 May 2017 Lizi Liao, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua Abstract Embedding
More informationLIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting
LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting El Moatez Billah Nagoudi Laboratoire d Informatique et de Mathématiques LIM Université Amar
More informationA Comparison of Two Text Representations for Sentiment Analysis
010 International Conference on Computer Application and System Modeling (ICCASM 010) A Comparison of Two Text Representations for Sentiment Analysis Jianxiong Wang School of Computer Science & Educational
More informationarxiv: v1 [cs.cl] 2 Apr 2017
Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings Junki Matsuo and Mamoru Komachi Graduate School of System Design, Tokyo Metropolitan University, Japan matsuo-junki@ed.tmu.ac.jp,
More informationUsing dialogue context to improve parsing performance in dialogue systems
Using dialogue context to improve parsing performance in dialogue systems Ivan Meza-Ruiz and Oliver Lemon School of Informatics, Edinburgh University 2 Buccleuch Place, Edinburgh I.V.Meza-Ruiz@sms.ed.ac.uk,
More informationarxiv: v1 [cs.cv] 10 May 2017
Inferring and Executing Programs for Visual Reasoning Justin Johnson 1 Bharath Hariharan 2 Laurens van der Maaten 2 Judy Hoffman 1 Li Fei-Fei 1 C. Lawrence Zitnick 2 Ross Girshick 2 1 Stanford University
More informationLinking Task: Identifying authors and book titles in verbose queries
Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,
More informationCSL465/603 - Machine Learning
CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am
More informationAQUA: An Ontology-Driven Question Answering System
AQUA: An Ontology-Driven Question Answering System Maria Vargas-Vera, Enrico Motta and John Domingue Knowledge Media Institute (KMI) The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom.
More informationActive Learning. Yingyu Liang Computer Sciences 760 Fall
Active Learning Yingyu Liang Computer Sciences 760 Fall 2017 http://pages.cs.wisc.edu/~yliang/cs760/ Some of the slides in these lectures have been adapted/borrowed from materials developed by Mark Craven,
More informationGeorgetown University at TREC 2017 Dynamic Domain Track
Georgetown University at TREC 2017 Dynamic Domain Track Zhiwen Tang Georgetown University zt79@georgetown.edu Grace Hui Yang Georgetown University huiyang@cs.georgetown.edu Abstract TREC Dynamic Domain
More informationDeep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach
#BaselOne7 Deep search Enhancing a search bar using machine learning Ilgün Ilgün & Cedric Reichenbach We are not researchers Outline I. Periscope: A search tool II. Goals III. Deep learning IV. Applying
More informationEvolutive Neural Net Fuzzy Filtering: Basic Description
Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:
More informationProbing for semantic evidence of composition by means of simple classification tasks
Probing for semantic evidence of composition by means of simple classification tasks Allyson Ettinger 1, Ahmed Elgohary 2, Philip Resnik 1,3 1 Linguistics, 2 Computer Science, 3 Institute for Advanced
More informationCross Language Information Retrieval
Cross Language Information Retrieval RAFFAELLA BERNARDI UNIVERSITÀ DEGLI STUDI DI TRENTO P.ZZA VENEZIA, ROOM: 2.05, E-MAIL: BERNARDI@DISI.UNITN.IT Contents 1 Acknowledgment.............................................
More informationMultilingual Sentiment and Subjectivity Analysis
Multilingual Sentiment and Subjectivity Analysis Carmen Banea and Rada Mihalcea Department of Computer Science University of North Texas rada@cs.unt.edu, carmen.banea@gmail.com Janyce Wiebe Department
More informationSemantic Segmentation with Histological Image Data: Cancer Cell vs. Stroma
Semantic Segmentation with Histological Image Data: Cancer Cell vs. Stroma Adam Abdulhamid Stanford University 450 Serra Mall, Stanford, CA 94305 adama94@cs.stanford.edu Abstract With the introduction
More informationPREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES
PREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES Po-Sen Huang, Kshitiz Kumar, Chaojun Liu, Yifan Gong, Li Deng Department of Electrical and Computer Engineering,
More informationarxiv: v1 [cs.lg] 15 Jun 2015
Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy arxiv:1506.04477v1 [cs.lg] 15 Jun 2015 Sang-Woo Lee Min-Oh Heo School of Computer Science and
More informationHIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION
HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION Atul Laxman Katole 1, Krishna Prasad Yellapragada 1, Amish Kumar Bedi 1, Sehaj Singh Kalra 1 and Mynepalli Siva Chaitanya 1 1 Samsung
More informationIntroduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition
Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and
More informationSpeech Recognition at ICSI: Broadcast News and beyond
Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI
More informationArtificial Neural Networks written examination
1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14
More informationarxiv: v5 [cs.ai] 18 Aug 2015
When Are Tree Structures Necessary for Deep Learning of Representations? Jiwei Li 1, Minh-Thang Luong 1, Dan Jurafsky 1 and Eduard Hovy 2 1 Computer Science Department, Stanford University, Stanford, CA
More informationThere are some definitions for what Word
Word Embeddings and Their Use In Sentence Classification Tasks Amit Mandelbaum Hebrew University of Jerusalm amit.mandelbaum@mail.huji.ac.il Adi Shalev bitan.adi@gmail.com arxiv:1610.08229v1 [cs.lg] 26
More informationTRANSFER LEARNING IN MIR: SHARING LEARNED LATENT REPRESENTATIONS FOR MUSIC AUDIO CLASSIFICATION AND SIMILARITY
TRANSFER LEARNING IN MIR: SHARING LEARNED LATENT REPRESENTATIONS FOR MUSIC AUDIO CLASSIFICATION AND SIMILARITY Philippe Hamel, Matthew E. P. Davies, Kazuyoshi Yoshii and Masataka Goto National Institute
More informationUsing the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees Cognitive Skills in Algebra on the SAT
The Journal of Technology, Learning, and Assessment Volume 6, Number 6 February 2008 Using the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees Cognitive Skills in Algebra on the
More informationJoint Learning of Character and Word Embeddings
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 205) Joint Learning of Character and Word Embeddings Xinxiong Chen,2, Lei Xu, Zhiyuan Liu,2, Maosong Sun,2,
More informationLearning Methods for Fuzzy Systems
Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8
More informationIterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages
Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages Nuanwan Soonthornphisaj 1 and Boonserm Kijsirikul 2 Machine Intelligence and Knowledge Discovery Laboratory Department of Computer
More informationCalibration of Confidence Measures in Speech Recognition
Submitted to IEEE Trans on Audio, Speech, and Language, July 2010 1 Calibration of Confidence Measures in Speech Recognition Dong Yu, Senior Member, IEEE, Jinyu Li, Member, IEEE, Li Deng, Fellow, IEEE
More informationThe Good Judgment Project: A large scale test of different methods of combining expert predictions
The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania
More informationRule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
More informationConstructing Parallel Corpus from Movie Subtitles
Constructing Parallel Corpus from Movie Subtitles Han Xiao 1 and Xiaojie Wang 2 1 School of Information Engineering, Beijing University of Post and Telecommunications artex.xh@gmail.com 2 CISTR, Beijing
More informationThe Karlsruhe Institute of Technology Translation Systems for the WMT 2011
The Karlsruhe Institute of Technology Translation Systems for the WMT 2011 Teresa Herrmann, Mohammed Mediani, Jan Niehues and Alex Waibel Karlsruhe Institute of Technology Karlsruhe, Germany firstname.lastname@kit.edu
More informationTruth Inference in Crowdsourcing: Is the Problem Solved?
Truth Inference in Crowdsourcing: Is the Problem Solved? Yudian Zheng, Guoliang Li #, Yuanbing Li #, Caihua Shan, Reynold Cheng # Department of Computer Science, Tsinghua University Department of Computer
More informationTRANSFER LEARNING OF WEAKLY LABELLED AUDIO. Aleksandr Diment, Tuomas Virtanen
TRANSFER LEARNING OF WEAKLY LABELLED AUDIO Aleksandr Diment, Tuomas Virtanen Tampere University of Technology Laboratory of Signal Processing Korkeakoulunkatu 1, 33720, Tampere, Finland firstname.lastname@tut.fi
More informationUsing Web Searches on Important Words to Create Background Sets for LSI Classification
Using Web Searches on Important Words to Create Background Sets for LSI Classification Sarah Zelikovitz and Marina Kogan College of Staten Island of CUNY 2800 Victory Blvd Staten Island, NY 11314 Abstract
More informationLanguage Independent Passage Retrieval for Question Answering
Language Independent Passage Retrieval for Question Answering José Manuel Gómez-Soriano 1, Manuel Montes-y-Gómez 2, Emilio Sanchis-Arnal 1, Luis Villaseñor-Pineda 2, Paolo Rosso 1 1 Polytechnic University
More informationMETHODS FOR EXTRACTING AND CLASSIFYING PAIRS OF COGNATES AND FALSE FRIENDS
METHODS FOR EXTRACTING AND CLASSIFYING PAIRS OF COGNATES AND FALSE FRIENDS Ruslan Mitkov (R.Mitkov@wlv.ac.uk) University of Wolverhampton ViktorPekar (v.pekar@wlv.ac.uk) University of Wolverhampton Dimitar
More informationTHE world surrounding us involves multiple modalities
1 Multimodal Machine Learning: A Survey and Taxonomy Tadas Baltrušaitis, Chaitanya Ahuja, and Louis-Philippe Morency arxiv:1705.09406v2 [cs.lg] 1 Aug 2017 Abstract Our experience of the world is multimodal
More informationA JOINT MANY-TASK MODEL: GROWING A NEURAL NETWORK FOR MULTIPLE NLP TASKS
A JOINT MANY-TASK MODEL: GROWING A NEURAL NETWORK FOR MULTIPLE NLP TASKS Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka & Richard Socher The University of Tokyo {hassy, tsuruoka}@logos.t.u-tokyo.ac.jp
More informationNetpix: A Method of Feature Selection Leading. to Accurate Sentiment-Based Classification Models
Netpix: A Method of Feature Selection Leading to Accurate Sentiment-Based Classification Models 1 Netpix: A Method of Feature Selection Leading to Accurate Sentiment-Based Classification Models James B.
More informationLessons from a Massive Open Online Course (MOOC) on Natural Language Processing for Digital Humanities
Lessons from a Massive Open Online Course (MOOC) on Natural Language Processing for Digital Humanities Simon Clematide, Isabel Meraner, Noah Bubenhofer, Martin Volk Institute of Computational Linguistics
More informationMulti-Lingual Text Leveling
Multi-Lingual Text Leveling Salim Roukos, Jerome Quin, and Todd Ward IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 {roukos,jlquinn,tward}@us.ibm.com Abstract. Determining the language proficiency
More informationMULTILINGUAL INFORMATION ACCESS IN DIGITAL LIBRARY
MULTILINGUAL INFORMATION ACCESS IN DIGITAL LIBRARY Chen, Hsin-Hsi Department of Computer Science and Information Engineering National Taiwan University Taipei, Taiwan E-mail: hh_chen@csie.ntu.edu.tw Abstract
More informationHuman Emotion Recognition From Speech
RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati
More informationFinding Translations in Scanned Book Collections
Finding Translations in Scanned Book Collections Ismet Zeki Yalniz Dept. of Computer Science University of Massachusetts Amherst, MA, 01003 zeki@cs.umass.edu R. Manmatha Dept. of Computer Science University
More informationSemi-Supervised Face Detection
Semi-Supervised Face Detection Nicu Sebe, Ira Cohen 2, Thomas S. Huang 3, Theo Gevers Faculty of Science, University of Amsterdam, The Netherlands 2 HP Research Labs, USA 3 Beckman Institute, University
More informationCROSS-LANGUAGE INFORMATION RETRIEVAL USING PARAFAC2
1 CROSS-LANGUAGE INFORMATION RETRIEVAL USING PARAFAC2 Peter A. Chew, Brett W. Bader, Ahmed Abdelali Proceedings of the 13 th SIGKDD, 2007 Tiago Luís Outline 2 Cross-Language IR (CLIR) Latent Semantic Analysis
More informationSwitchboard Language Model Improvement with Conversational Data from Gigaword
Katholieke Universiteit Leuven Faculty of Engineering Master in Artificial Intelligence (MAI) Speech and Language Technology (SLT) Switchboard Language Model Improvement with Conversational Data from Gigaword
More informationModeling function word errors in DNN-HMM based LVCSR systems
Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford
More informationRule Learning with Negation: Issues Regarding Effectiveness
Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX
More informationCultivating DNN Diversity for Large Scale Video Labelling
Cultivating DNN Diversity for Large Scale Video Labelling Mikel Bober-Irizar mikel@mxbi.net Sameed Husain sameed.husain@surrey.ac.uk Miroslaw Bober m.bober@surrey.ac.uk Eng-Jon Ong e.ong@surrey.ac.uk Abstract
More informationEnsemble Technique Utilization for Indonesian Dependency Parser
Ensemble Technique Utilization for Indonesian Dependency Parser Arief Rahman Institut Teknologi Bandung Indonesia 23516008@std.stei.itb.ac.id Ayu Purwarianti Institut Teknologi Bandung Indonesia ayu@stei.itb.ac.id
More informationDialog-based Language Learning
Dialog-based Language Learning Jason Weston Facebook AI Research, New York. jase@fb.com arxiv:1604.06045v4 [cs.cl] 20 May 2016 Abstract A long-term goal of machine learning research is to build an intelligent
More informationTarget Language Preposition Selection an Experiment with Transformation-Based Learning and Aligned Bilingual Data
Target Language Preposition Selection an Experiment with Transformation-Based Learning and Aligned Bilingual Data Ebba Gustavii Department of Linguistics and Philology, Uppsala University, Sweden ebbag@stp.ling.uu.se
More informationPredicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks
Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks Devendra Singh Chaplot, Eunhee Rhim, and Jihie Kim Samsung Electronics Co., Ltd. Seoul, South Korea {dev.chaplot,eunhee.rhim,jihie.kim}@samsung.com
More informationSummarizing Answers in Non-Factoid Community Question-Answering
Summarizing Answers in Non-Factoid Community Question-Answering Hongya Song Zhaochun Ren Shangsong Liang hongya.song.sdu@gmail.com zhaochun.ren@ucl.ac.uk shangsong.liang@ucl.ac.uk Piji Li Jun Ma Maarten
More information