# Training a Neural Network to Answer 8th Grade Science Questions Steven Hewitt, An Ju, Katherine Stasaski

Size: px
Start display at page:

Transcription

3 The model is composed of four modules: 1. Form Hypothesis (FH) : Given a question and an answer choice, the FH module will generate a hypothesis. For example, given the question Which of the following does not allow sound to travel through? and an answer choice vacuum, FH will output the hypothesis, vacuum does not allow sound to travel through. The FH module is implemented mainly with regular expression substitution. 2. Find Evidence (FE) : Given a hypothesis, the FE module will search textbooks for supporting evidence, i.e. a sentence that supports or refutes the hypothesis sentence. For example, given the hypothesis vacuum does not allow sound to travel through., the FE module will return several sentences from textbooks such as sound cannot travel in vacuum, sound must travel through air, and vacuum means the absence of air in an environment. The FE model will also return a confidence score for each piece of evidence. The FE module is implemented with PyLucene. 3. Entailment (E) : The Entailment model is a model that computes the entailment relation between two input vectorized strings. If sentence A entails sentence B, it means that given the information in Sentence A, the information presented in Sentence B logically follows. On the other hand, if Sentence A contradicts Sentence B, it means that given Sentence A, Sentence B cannot logically follow. For each sentence pair, the output is a probability distribution over three categories: contradiction, entailment, and neutral. This distribution is further converted into a single score in which -1 means contradiction, 0 means neutral, and 1 means entailment. Each one of the four models were tested as this part of the pipeline. 4. Evidence Weighing (EW) : Given the confidence c and the entailment score r for all evidence, EM will compute a single score for the answer: y = c i r i i The final answer is chosen as the output with maximum y value. Baseline Model The Baseline model, shown in Figure 2, is a bidirectional RNN which takes as input the two sentences concatenated, has dropout on the word level, and passes the sentence embedding to a fully connected layer with dropout and regularization to determine entailment. Figure 2: Baseline model architecture 3

4 Siamese Model The Siamese model, shown in Figure 3, is a similar approach to the Baseline Model. However, sentence embeddings are generated separately, and the difference vector is fed in input to a fully-connected layer to determine entailment [6]. We hypothesized that by generating sentence embeddings separately and having the fully connected layer compare the two embeddings, the entailment model would be more robust and outperform the baseline model. Figure 3: Siamese model architecture. MFF Model We wanted to compare our sequence-based model to a non-sequence model, based on Parikh et al [7]. We implemented a multi feed-forward model, shown in Figure 4. This model creates an attention map with the two sentences and uses several feed-forward neural networks to synthesize and determine entailment. As is shown in Figure 4, each of three modules contains a nonlinear function, which is implemented as a feed-forward network with ReLU layer. Hidden layers of these networks are of the same size. We have tested two parameters: MFF-32, which has a hidden layer of size 32, and MFF-64, which has a hidden layer of size 64. Figure 4: MFF model architecture Convolutional Neural Network (CNN) Model As a variant of MFF model, instead of feed-forward network, we propose to use a convolutional neural network to synthesize attention information, shown in Figure 5. We use multiple attention maps, so that we can capture different types of word correlation. Each attention map is treated as an input channel for CNN. CNN will capture the local information and global information of attention maps. A FC layer with softmax is used at 4

5 the end the the network to output a probability distribution over three categories. Limited to computing resources, we only use one CNN layer with one 1 1 kernel, which combines channels into a single matrix. Figure 5: CNN Model architecture General Question Answering Models In addition to comparing multiple entailment models, we wanted to compare results to entirely different approach to question answering, which searches through a paragraph of information for the answer to a question instead of comparing two sentences to determine entailment. Dynamic Memory Network The Dynamic Memory Network (DMN) model is introduced by Kumar et al in 2016 [8]. It is composed of five parts: semantic memory module, input module, question module, episodic memory module, and answer module, as is shown in Figure 6. Episodic Memory Module is the core module of DMN. This module works as a soft attention on facts (evidence from PyLucene in our case), and is designed to emulate the change of human attentions over time when answering questions. Attentions are controlled by a recurrent neural network, which is initialized by the question embedding and takes as input the synthesized information on facts with current attention. As is shown in the paper, this module can also provide a very beautiful visualization of question answering process. The attention changes over time, following exactly the same way a human would answer the question, which can be interpreted as the model is doing reasoning on facts. Figure 6: Architecture of DMN Model [8]. 5

6 End to End Memory Network In order to compare the DMN to another type of QA system, we also tested with an end-to-end (E2E) memory network, as introduced by Bordes et al in 2015 [9]. The E2E memory system relies on a memory structure and a number of hops around the input passage to reason about the question and produce an answer. In the original model implementation, the data was labeled with which passage lines were necessary to answer the question. However, we did not have the time or bandwidth to hand-label all of the test and training passages with relevant line numbers. Therefore, we ran this model with two different data settings -- one which listed all lines in the passage as being necessary to attend to and one which listed none of them. We wanted to compare the two settings and see if insights could be gained by running the model regardless. Figure 7: (a) A single layer of the E2E network (b) The combination of three layers that comprise the final model [9] Results As a preliminary step, we evaluated our entailment models with the SNLI dataset, which is a collection of sentences catered specifically for entailment [4]. Each pair of sentences is accompanied by a label (either entailment, contradiction, or neutral) as well as a confidence score. This was done to see if our models could successfully complete an entailment task independent of the science question pipeline. Results are summarized in Figure 8. Figure 8: Results of each model on SNLI Entailment Data 6

7 After confirming that our models could complete entailment tasks, we turned to testing our models on the AI2 science question data. Results for these experiments can be seen in Figure 9. Figure 9: Results of each model on AI2 Science Question Data. (Note: E2E w/ lines has all lines included in attention while E2E no lines has none.) The non-neural Tf-Idf baseline was computed by choosing the answer choice whose hypothesis had the highest Tf-Idf similarity to a sentence from the textbook. Lessons Learned Through running experiments and analyzing performance of the six models we ultimately fully implemented yielded sub-par results. In fact, most of our neural models were not able to surpass the non-neural Tf-idf baseline of 33%. Thus, we explored why our models were not performing and hypothesized several reasons why. Question Difficulties One hypothesis about why our models were not performing well on the AI2 data was that the types of questions included in the training and test sets were such that there was not a one optimal network that would work for each question in the training or test set. We examined the questions by hand and completed two different ways to evaluate questions--the question length and the type of question. Each of these were compared using one representative sequence-based model (baseline) and one non-sequence model (MFF). Question length refers to whether or not a question includes one or more informative sentences that are necessary to answer the question. For example, the question The metal lid on a glass jar is hard to open so it is held under warm running water. What causes the jar to open easily after it was held under the water? includes information from the first sentence in the question to choose the correct answer choice. Results for this can be seen in Figure 10. Our hypothesis was that both networks would perform better on shorter questions, as these are typically more conceptual and require less memory ability. This held true for the MFF model, but an interesting result was the Baseline model s comparative advantage with long questions. Even though MFF outperforms the long question performance, the Baseline model does better on 7

8 longer questions than shorter questions. Thus, it may be beneficial to split questions and train networks specifically to answer longer or shorter questions. Question type refers to the category of question, determined by the wh word and other key words included in the questions, and results can be seen in Figure 11. It is evident that each model has certain strengths and other shortcomings. Because of this, it may be worth exploring a multi-network approach which trains multiple networks each on a single type of question. Figure 10: The accuracy of the two representative models on questions of different lengths. Figure 11: The accuracy of the two representative models on questions of different types. Model Shortcomings Entailment Models The significant deterioration of performance when switching from entailment data to science question data leads us to believe that entailment might not have been the right approach to this problem. One obvious shortcoming of an entailment model is that it is designed to compare two sentences to each other directly. This means that any information we want the network to use to choose a given answer choice has to be perfectly captured in one sentence. This, however, is not the case with many questions in the dataset. The questions either require information from more than one sentence or require a level of complex reasoning that an entailment model does not capture. General QA Models The memory-based QA models were implemented as a first step to test the hypothesis that entailment models were not the optimal way to answer 8th grade science questions. Memory-based methods work best when the specific lines in the passage that the network should 8

10 Team Contributions and Workload Percentage An Ju (⅓): Wrote basic training structure in TF. Integrated and tested the baseline model. Wrote scripts to speedup training. Wrote scripts to test model modules. Wrote Siamese, MFF, DNN models in TF. Helped write DMN model in TF. Hyperparameter tuning. Steven Hewitt (⅓): Created and improved GloVe-based word vectorization, improved upon hypothesis gathering, and wrote data gathering scripts. Helped write DMN model. Hyperparameter tuning. Katherine Stasaski (⅓): Created initial evidence retrieval method from textbooks, improved evidence gathering by switching to PyLucene, created first version of entailment model in TF (later improved by An), pre-processed data, created naive hypothesis generator (later improved by Steven), found MFF paper, created end to end question answering model in TF. Hyperparameter tuning. References [1] Sachan, M., Dubey, A., & Xing, E. P. (n.d.). Science Question Answering using Instructional Materials, [2] Baudis, Petr, Silvestr Stanko, and Jan Sedivy Joint Learning of Sentence Embeddings for Relevance and Entailment, [3] [4] Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). [5] Jeffrey Pennington, Richard Socher, and Christopher D. Manning GloVe: Global Vectors for Word Representation [6] J. Mueller and A. Thyagarajan, Siamese Recurrent Architecture for Learning Sentence Similarity, AAAI, [7] Parikh, Ankur P, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit A Decomposable Attention Model for Natural Language Inference. arxiv. [8] Kumar, Ankit, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher Ask Me Anything: Dynamic Memory Networks for Natural Language Processing. Nips. [9] Bordes, Antoine, Usunier, Nicolas, Chopra, Sumit, and Weston, Jason. Large-scale simple question answering with memory networks. arxiv preprint arxiv: , [10] May, Rob. "How We Approached The Allen A.I. Challenge on Kaggle." How We Approached The Allen A.I. Challenge on Kaggle. N.p., 11 Jan Web. 14 Dec [11] Vorontsov, Konstantin. DeepHack.Q&A Konstantin Vorontsov Regularization of Topic Models for Question Answering. YouTube. 01 Feb Web. 14 Dec [12] "Implementing Dynamic Memory Networks." Implementing Dynamic Memory Networks YerevaNN. YerevaNN, 05 Feb Web. 14 Dec

### 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

### A 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

### Ask 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

### Python 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

### Modeling 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

### arxiv: 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

### arxiv: 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

### Assignment 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

### A 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

### Dialog-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

### Unsupervised 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.

### arxiv: v3 [cs.cl] 7 Feb 2017

NEWSQA: A MACHINE COMPREHENSION DATASET Adam Trischler Tong Wang Xingdi Yuan Justin Harris Alessandro Sordoni Philip Bachman Kaheer Suleman {adam.trischler, tong.wang, eric.yuan, justin.harris, alessandro.sordoni,

### ON 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

### Глубокие 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,

### Lecture 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

### Module 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

### Learning 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

### POS 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

### Second 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

### Modeling 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

### arxiv: v1 [cs.lg] 7 Apr 2015

Transferring Knowledge from a RNN to a DNN William Chan 1, Nan Rosemary Ke 1, Ian Lane 1,2 Carnegie Mellon University 1 Electrical and Computer Engineering, 2 Language Technologies Institute Equal contribution

### Indian Institute of Technology, Kanpur

Indian Institute of Technology, Kanpur Course Project - CS671A POS Tagging of Code Mixed Text Ayushman Sisodiya (12188) {ayushmn@iitk.ac.in} Donthu Vamsi Krishna (15111016) {vamsi@iitk.ac.in} Sandeep Kumar

### arxiv: 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,

### Deep 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

### Georgetown 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

### A 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

### arxiv: 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

### A 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

### Knowledge 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

### There 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

### A Neural Network GUI Tested on Text-To-Phoneme Mapping

A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis

### Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models

Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Navdeep Jaitly 1, Vincent Vanhoucke 2, Geoffrey Hinton 1,2 1 University of Toronto 2 Google Inc. ndjaitly@cs.toronto.edu,

### AQUA: 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.

### Deep 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

### Model Ensemble for Click Prediction in Bing Search Ads

Model Ensemble for Click Prediction in Bing Search Ads Xiaoliang Ling Microsoft Bing xiaoling@microsoft.com Hucheng Zhou Microsoft Research huzho@microsoft.com Weiwei Deng Microsoft Bing dedeng@microsoft.com

### arxiv: 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

### Residual 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

### Semantic 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

### Semi-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

### Word Segmentation of Off-line Handwritten Documents

Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department

### HIERARCHICAL 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

### What is a Mental Model?

Mental Models for Program Understanding Dr. Jonathan I. Maletic Computer Science Department Kent State University What is a Mental Model? Internal (mental) representation of a real system s behavior,

### Speech 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

### Online Updating of Word Representations for Part-of-Speech Tagging

Online Updating of Word Representations for Part-of-Speech Tagging Wenpeng Yin LMU Munich wenpeng@cis.lmu.de Tobias Schnabel Cornell University tbs49@cornell.edu Hinrich Schütze LMU Munich inquiries@cislmu.org

### TRANSFER 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

### BUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING

BUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING Gábor Gosztolya 1, Tamás Grósz 1, László Tóth 1, David Imseng 2 1 MTA-SZTE Research Group on Artificial

### SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF)

SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) Hans Christian 1 ; Mikhael Pramodana Agus 2 ; Derwin Suhartono 3 1,2,3 Computer Science Department,

### Probabilistic 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

### Human 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

### OPTIMIZATINON 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,

### have to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words,

A Language-Independent, Data-Oriented Architecture for Grapheme-to-Phoneme Conversion Walter Daelemans and Antal van den Bosch Proceedings ESCA-IEEE speech synthesis conference, New York, September 1994

### Semantic 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

### Probing 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

### arxiv: v1 [cs.cl] 27 Apr 2016

The IBM 2016 English Conversational Telephone Speech Recognition System George Saon, Tom Sercu, Steven Rennie and Hong-Kwang J. Kuo IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598 gsaon@us.ibm.com

### Rule 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

### Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Cristina Vertan, Walther v. Hahn University of Hamburg, Natural Language Systems Division Hamburg,

### FUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria

FUZZY EXPERT SYSTEMS 16-18 18 February 2002 University of Damascus-Syria Dr. Kasim M. Al-Aubidy Computer Eng. Dept. Philadelphia University What is Expert Systems? ES are computer programs that emulate

### Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1

Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17-652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial

### THE 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

### QuickStroke: 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

### Attributed 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

### Chinese 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

### EQuIP Review Feedback

EQuIP Review Feedback Lesson/Unit Name: On the Rainy River and The Red Convertible (Module 4, Unit 1) Content Area: English language arts Grade Level: 11 Dimension I Alignment to the Depth of the CCSS

### arxiv: 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

### arxiv: 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,

### Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-6) Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors Sang-Woo Lee,

### A 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,

### Cultivating 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

### Diverse Concept-Level Features for Multi-Object Classification

Diverse Concept-Level Features for Multi-Object Classification Youssef Tamaazousti 12 Hervé Le Borgne 1 Céline Hudelot 2 1 CEA, LIST, Laboratory of Vision and Content Engineering, F-91191 Gif-sur-Yvette,

### Beyond the Pipeline: Discrete Optimization in NLP

Beyond the Pipeline: Discrete Optimization in NLP Tomasz Marciniak and Michael Strube EML Research ggmbh Schloss-Wolfsbrunnenweg 33 69118 Heidelberg, Germany http://www.eml-research.de/nlp Abstract We

### Twitter 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

### PUBLIC CASE REPORT Use of the GeoGebra software at upper secondary school

PUBLIC CASE REPORT Use of the GeoGebra software at upper secondary school Linked to the pedagogical activity: Use of the GeoGebra software at upper secondary school Written by: Philippe Leclère, Cyrille

MYCIN Developed at Stanford University in 1972 Regarded as the first true expert system Assists physicians in the treatment of blood infections Many revisions and extensions over the years The MYCIN Task

### Predicting 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

### Product Feature-based Ratings foropinionsummarization of E-Commerce Feedback Comments

Product Feature-based Ratings foropinionsummarization of E-Commerce Feedback Comments Vijayshri Ramkrishna Ingale PG Student, Department of Computer Engineering JSPM s Imperial College of Engineering &

### Comment-based Multi-View Clustering of Web 2.0 Items

Comment-based Multi-View Clustering of Web 2.0 Items Xiangnan He 1 Min-Yen Kan 1 Peichu Xie 2 Xiao Chen 3 1 School of Computing, National University of Singapore 2 Department of Mathematics, National University

### Learning From the Past with Experiment Databases

Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University

### Rule 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

### Multi-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

### The IDN Variant Issues Project: A Study of Issues Related to the Delegation of IDN Variant TLDs. 20 April 2011

The IDN Variant Issues Project: A Study of Issues Related to the Delegation of IDN Variant TLDs 20 April 2011 Project Proposal updated based on comments received during the Public Comment period held from

CHUNG AND ZISSERMAN: BMVC AUTHOR GUIDELINES 1 Lip Reading in Profile Joon Son Chung http://wwwrobotsoxacuk/~joon Andrew Zisserman http://wwwrobotsoxacuk/~az Visual Geometry Group Department of Engineering

### The University of Amsterdam s Concept Detection System at ImageCLEF 2011

The University of Amsterdam s Concept Detection System at ImageCLEF 2011 Koen E. A. van de Sande and Cees G. M. Snoek Intelligent Systems Lab Amsterdam, University of Amsterdam Software available from:

### Bridging Lexical Gaps between Queries and Questions on Large Online Q&A Collections with Compact Translation Models

Bridging Lexical Gaps between Queries and Questions on Large Online Q&A Collections with Compact Translation Models Jung-Tae Lee and Sang-Bum Kim and Young-In Song and Hae-Chang Rim Dept. of Computer &

### Longest Common Subsequence: A Method for Automatic Evaluation of Handwritten Essays

IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. IV (Nov Dec. 2015), PP 01-07 www.iosrjournals.org Longest Common Subsequence: A Method for

### Chapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA. 1. Introduction. Alta de Waal, Jacobus Venter and Etienne Barnard

Chapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA Alta de Waal, Jacobus Venter and Etienne Barnard Abstract Most actionable evidence is identified during the analysis phase of digital forensic investigations.

### A 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

### A Case-Based Approach To Imitation Learning in Robotic Agents

A Case-Based Approach To Imitation Learning in Robotic Agents Tesca Fitzgerald, Ashok Goel School of Interactive Computing Georgia Institute of Technology, Atlanta, GA 30332, USA {tesca.fitzgerald,goel}@cc.gatech.edu

### Postprint.

http://www.diva-portal.org Postprint This is the accepted version of a paper presented at CLEF 2013 Conference and Labs of the Evaluation Forum Information Access Evaluation meets Multilinguality, Multimodality,

### A 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

### CS224d Deep Learning for Natural Language Processing. Richard Socher, PhD

CS224d Deep Learning for Natural Language Processing, PhD Welcome 1. CS224d logis7cs 2. Introduc7on to NLP, deep learning and their intersec7on 2 Course Logis>cs Instructor: (Stanford PhD, 2014; now Founder/CEO

### Speech Emotion Recognition Using Support Vector Machine

Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,

### Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture

Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture Yuanliang Meng, Anna Rumshisky, Alexey Romanov {ymeng,arum,aromanov}@cs.uml.edu Department

### Extracting Opinion Expressions and Their Polarities Exploration of Pipelines and Joint Models

Extracting Opinion Expressions and Their Polarities Exploration of Pipelines and Joint Models Richard Johansson and Alessandro Moschitti DISI, University of Trento Via Sommarive 14, 38123 Trento (TN),

### Word 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

### Machine Learning from Garden Path Sentences: The Application of Computational Linguistics

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics http://dx.doi.org/10.3991/ijet.v9i6.4109 J.L. Du 1, P.F. Yu 1 and M.L. Li 2 1 Guangdong University of Foreign Studies,

### Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration

INTERSPEECH 2013 Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration Yan Huang, Dong Yu, Yifan Gong, and Chaojun Liu Microsoft Corporation, One

### TIMSS ADVANCED 2015 USER GUIDE FOR THE INTERNATIONAL DATABASE. Pierre Foy

TIMSS ADVANCED 2015 USER GUIDE FOR THE INTERNATIONAL DATABASE Pierre Foy TIMSS Advanced 2015 orks User Guide for the International Database Pierre Foy Contributors: Victoria A.S. Centurino, Kerry E. Cotter,