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• Syntax and syntactic analysis
• Semantics and semantic analysis.-Spoken language and dialogue
• Corpora and corpus-based language processing
• Text and message understanding
• Information extraction and text mining
• Information retrieval and question answering
• Language learning
• Machine translation.

This book constitutes the refereed proceedings of the 14th International Conference of the Pacific Association for Computational Linguistics, PACLING 2015, held in Bali, Indonesia, in May 2015. The 18 revised full papers presented were carefully reviewed and selected from 45 papers. The papers are organized around the following topics: syntax and syntactic analysis; semantics and semantic analysis; spoken language and dialogue; corpora and corpus-based language processing; text and message understanding; information extraction and text mining; information retrieval and question answering; language learning; machine translation.

• Phraseology in translation and contrastive studies.- Lexicography and terminography.- Exploitation of corpora in phraseological studies.- Development of corpora for phraseological studies.- Phraseology and language learning.- Cognitive and cultural aspects of phraseology.- Theoretical and descriptive approaches to phraseology.- Computational approaches to phraseology.
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This book constitutes the refereed proceedings of the International Conference on Computational and Corpus-Based Phraseology, Europhras 2017, held in London, UK, in November 2017. The 31 full papers presented were carefully reviewed and selected from numerous submissions and are organized into the following thematic sessions: Phraseology in translation and contrastive studies, Lexicography and terminography, Exploitation of corpora in phraseological studies, Development of corpora for phraseological studies, Phraseology and language learning, Cognitive and cultural aspects of phraseology, Theoretical and descriptive approaches to phraseology, and Computational approaches to phraseology. The chapter 'Frequency Consolidation Among Word N-Grams' is available open access under a CC BY 4.0 license.
(source: Nielsen Book Data)

• Linguistic Decision Making and Computing with Words
• 2-tuple Linguistic Model
• Linguistic Approaches Based on the 2-tuple Fuzzy Linguistic Representation Model
• Decision Making in Heterogeneous Context: 2-tuple Linguistic Based Approaches
• Decision Making with Unbalanced Linguistic Information
• Dealing with Hesitant Fuzzy Linguistic Information in Decision Making
• 2-tuple Linguistic Decision Based Applications
• FLINTSTONES: A Fuzzy LINguiSTic decisiON tools Enhancement Suite.

This book examines one of the more common and wide-spread methodologies to deal with uncertainty in real-world decision making problems, the computing with words paradigm, and the fuzzy linguistic approach. The 2-tuple linguistic model is the most popular methodology for computing with words (CWW), because it improves the accuracy of the linguistic computations and keeps the interpretability of the results. The authors provide a thorough review of the specialized literature in CWW and highlight the rapid growth and applicability of the 2-tuple linguistic model. They explore the foundations and methodologies for CWW in complex frameworks and extensions. The book introduces the software FLINTSTONES that provides tools for solving linguistic decision problems based on the 2-tuple linguistic model. Professionals and researchers working in the field of classification or fuzzy sets and systems will find The 2-tuple Linguistic Model: Computing with Words in Decision Making a valuable resource. Undergraduate and postdoctoral students studying computer science and statistics will also find this book a useful study guide.

• Table of Contents Recognizing traffic signs using Convnets Annotating Images with Object Detection API Caption generation for images Building GANs for Conditional Image Creation Stock Price Prediction with LSTM Create & Train Machine Translation Systems Train and set up a Chatbot, able to discuss like a human Detecting Duplicate Quora Questions Building a TensorFlow Recommender Systems Video Games by Reinforcement learning.
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Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios Key Features Build efficient deep learning pipelines using the popular Tensorflow framework Train neural networks such as ConvNets, generative models, and LSTMs Includes projects related to Computer Vision, stock prediction, chatbots and more Book DescriptionTensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently. What you will learn Set up the TensorFlow environment for deep learning Construct your own ConvNets for effective image processing Use LSTMs for image caption generation Forecast stock prediction accurately with an LSTM architecture Learn what semantic matching is by detecting duplicate Quora questions Set up an AWS instance with TensorFlow to train GANs Train and set up a chatbot to understand and interpret human input Build an AI capable of playing a video game by itself -and win it! Who this book is forThis book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.
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• Natural Language Processing.- Machine Learning and Data Mining Applications.- Knowledge Representation, Reasoning and Search.
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This book constitutes the refereed proceedings of the 18th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2018, held in Varna, Bulgaria, in September 2018. The 22 revised full papers and 7 poster papers presented were carefully reviewed and selected from 72 submissions. They cover a wide range of topics in AI: from machine learning to natural language systems, from information extraction to text mining, from knowledge representation to soft computing; from theoretical issues to real-world applications.
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Every year since 2002, the linguistic development environment NooJ has been enhanced with new online features that allow social scientists to develop new applications and explore new domains. The 2011 conference was no exception and the arrival of v3.0 has brought many more features and a new range of applications, from the analysis of ancient Arabic and old English texts to the analysis of conversations held by the Mars500 mission's astronauts. At the 2011 conference, members of the European Meta-Net CESAR project announced that NooJ will soon be available Open Source and will become the de-facto standard tool for Corpus processing in European research in Social Science. Today, NooJ is used as a research tool in over 30 academic and research centers in the world and there are NooJ modules available for over 20 languages. The international NooJ conference is organized every year; 50 participants present their work in the domains of Linguistic formalization, Corpus processing and Natural Language Processing applications. The present volume contains a selection of papers from the NooJ 2011 International Conference which was held from 13-15 June 2011 in Dubrovnik, Croatia. This volume presents problems dealing with machine translation, information extraction, processing of multi-word units, automatic disambiguation, semantic analysis, and psychological and literature analysis of various corpora.
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• PART 1: Linguistic resources.-
• PART 2: Tools and related methodologies.
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The papers collected in this volume are selected as a sample of the progress in Natural Language Processing (NLP) performed within the Italian NLP community and especially attested by the PARLI project. PARLI (Portale per l'Accesso alle Risorse in Lingua Italiana) is a project partially funded by the Ministero Italiano per l'Universita e la Ricerca (PRIN 2008) from 2008 to 2012 for monitoring and fostering the harmonic growth and coordination of the activities of Italian NLP. It was proposed by various teams of researchers working in Italian universities and research institutions. According to the spirit of the PARLI project, most of the resources and tools created within the project and here described are freely distributed and they did not terminate their life at the end of the project itself, hoping they could be a key factor in future development of computational linguistics.
(source: Nielsen Book Data)

• Overview of Text Mining
• From Textual Information to Numerical Vectors
• Using Text for Prediction
• Information Retrieval and Text Mining
• Finding Structure in a Document Collection
• Looking for Information in Documents
• Data Sources for Prediction: Databases, Hybrid Data and the Web
• Case Studies
• Emerging Directions.

This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, and errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Topics and features: Presents a comprehensive, practical and easy-to-read introduction to text mining Includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter Explores the application and utility of each method, as well as the optimum techniques for specific scenarios Provides several descriptive case studies that take readers from problem description to systems deployment in the real world Describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English) Contains links to free downloadable industrial-quality text-mining software and other supplementary instruction material Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students.

• Table of Contents Why Deep Learning? Getting Yourself Ready for Deep Learning Getting Started with Neural Networks Deep Learning in Computer Vision NLP - Vector Representation Advanced Natural Language Processing Multimodality Deep Reinforcement Learning Deep Learning Hacks Deep Learning Trends.
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Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book DescriptionDeep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is forAspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.
(source: Nielsen Book Data)

• TABLE OF CONTENTS; LIST OF ABBREVIATIONS; PREFACE; CHAPTER ONE; CHAPTER TWO; CHAPTER THREE; CHAPTER FOUR; CHAPTER FIVE; CHAPTER SIX; CHAPTER SEVEN; CONTRIBUTORS; INDEX.

This book inaugurates a series of discussions on what is permanent in the original thinking of the UNL - Universal Networking Language - and the changes that have been introduced during its development. The purpose of the book is to highlight the UNL's fundamental principles that remain as integral as they were when they were first formulated several years ago, while showing how their materialization has evolved over time, following the advances in Linguistics, Knowledge Engineering and Information Sciences. The fundamental and unchanged principles of the UNL are: the idea of an artificial lan.

This step-by-step guide demonstrates how to build simple-to-advanced applications through examples in R using modern tools. About This Book * Get a firm hold on the fundamentals of R through practical hands-on examples * Get started with good R programming fundamentals for data science * Exploit the different libraries of R to build interesting applications in R Who This Book Is For This books is for aspiring data science professionals or statisticians who would like to learn about the R programming language in a practical manner. Basic programming knowledge is assumed. What You Will Learn * Discover techniques to leverage R's features, and work with packages * Perform a descriptive analysis and work with statistical models using R * Work efficiently with objects without using loops * Create diverse visualizations to gain better understanding of the data * Understand ways to produce good visualizations and create reports for the results * Read and write data from relational databases and REST APIs, both packaged and unpackaged * Improve performance by writing better code, delegating that code to a more efficient programming language, or making it parallel In Detail R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R. We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization. By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable. Style and Approach This is an easy-to-understand guide filled with real-world examples, giving you a holistic view of R and practical, hands-on experience.
(source: Nielsen Book Data)

• Linguistically motivated morphological analysis and Generation
• Computational frameworks for implementing such Systems
• Linguistic frameworks suitable for computational implementation
• Research on very underresourced languages
• Interactions between computational morphology and formal, quantitative, and descriptive morphology.- Applications of computational morphology in the Digital Humanities.

This book constitutes the refereed proceedings of the 4th International Workshop on Systems and Frameworks for Computational Morphology, SFCM 2015, held in Stuttgart, Germany, in September 2015. The 5 revised full papers and 5 short papers presented were carefully reviewed and selected from 16 submissions. The SFCM Workshops focus on linguistically motivated morphological analysis and generation, computational frameworks for implementing such systems, and linguistic frameworks suitable for computational implementation. SFCM 2015 and the papers presented in this volume aim at broadening the scope to include research on very underresourced languages, interactions between computational morphology and formal, quantitative, and descriptive morphology, as well as applications of computational morphology in the Digital Humanities.

• Database and Distributed Machine Learning.- Querying and Mining Strings Made Easy Distributed Training Large-Scale Deep Architectures.- Fault Detection and Localization in Distributed Systems using Recurrent Convolutional Neural Networks.- Discovering Group Skylines with Constraints by Early Candidate Pruning.- Comparing MapReduce-Based k-NN Similarity Joins On Hadoop For High-dimensional Data.- A Higher-Fidelity Frugal Quantile Estimator.- Recommender System.- Fair Recommendations Through Diversity Promotion.- A Hierarchical Bayesian Factorization Model for Implicit and Explicit Feedback Data.- Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities.- Group Recommender Model Based on Preference Interaction.- Identification of Grey Sheep Users By Histogram Inter
• section In Recommender Systems.- Social Network and Social Media.- A Feature-based Approach for the Redefined Link Prediction Problem in Signed Networks.- From Mutual Friends to Overlapping Community Detection: A Non-negative Matrix Factorization Approach.- Calling for Response: Automatically Distinguishing Situation-aware Tweets During Crises.- Efficient Revenue Maximization for Viral Marketing in Social Networks.- Generating Life Course Trajectory Sequences with Recurrent Neural Networks and Application to Early Detection on Social Disadvantage.- FRISK: A Multilingual Approach to Find twitteR InterestS via wiKipedia.- A Solution to Tweet-Based User Identification across Online Social Networks.- Machine Learning.- Supervised Feature Selection Algorithm Based on Low-Rank and Manifold Learning.- Mixed Membership Sparse Gaussian Conditional Random Fields.- Effects of Dynamic Subspacing in Random Forest.- Diversity and Locality in Multi-Component, Multi-Layer Predictive Systems: A Mutual Information Based Approach.- Hybrid Subspace Mixture Models For Prediction and Anomaly Detection in High Dimensions.- Classification and Clustering Methods.- StruClus: Scalable Structural Graph Set Clustering with Representative Sampling.- Employing Hierarchical Clustering and Reinforcement Learning for Attribute-based Zero-Shot Classification.- Environmental Sound Recognition using Masked Conditional Neural Networks.- Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure.- A Framework for Clustering and Dynamic Maintenance of XML Documents.- Language-independent Twitter Classification using Character-based Convolutional Networks.- Behavior Modeling and User Profiling.- Modeling Check-in Behavior with Geographical Neighborhood Influence of Venues.- An empirical study on collective online behaviors of extremist supporters. -Your Moves, Your Device: Establishing Behavior Profiles using Tensors.- An Approach for Identifying Author Profiles of Blogs.- Generating Topics of Interests for Research Communities.- An Evolutionary Approach for Learning Conditional Preference Network from Inconsistent Examples.- Bioinformatic and Medical Data Analysis.- Predicting Clinical Outcomes of Alzheimer's Disease from Complex Brain Networks.- Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties.- Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets.- Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers.- Spatio-temporal Data.- People-Centric Mobile Crowdsensing Platform for Urban Design.- Long-Term User Location Prediction Using Deep Learning and Periodic Pattern Mining.- An Intelligent Weighted Fuzzy Time Series Model Based on A Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting.- Mobile Robot Scheduling with Multiple Trips and Time Windows.- Natural Language Processing and Text Mining.- Feature Analysis for Duplicate Detection in Programming QA Communities.- A Joint Human/Machine Process for Coding Events and Conflict Drivers.- Quality Prediction of Newly Proposed Questions in CQA by Leveraging Weakly Supervised Learning.- Improving Chinese Sentiment Analysis via Segmentation-based Representation Using Parallel CNN.- Entity Recognition by Distant Supervision with Soft List Constraint.- Structured Sentiment Analysis.- Data Mining Applications.- Improving Real-Time Bidding Using a Constrained Markov Decision Process.- PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network.- Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments.- Color-sketch simulator: a guide for color-based visual known-item search.- Applications.- Making Use of External Company Data to Improve the Classification of Bank Transactions.- Mining Load Profile Patterns for Australian Electricity Consumers.- STA: a Spatio-temporal Thematic Analytics Framework for Urban Ground Sensing.- Privacy and Utility Preservation for Location Data Using Stay Region Analysis.- Location-aware Human Activity Recognition.- Demos.- SWYSWYK: a new Sharing Paradigm for the Personal Cloud.- Tools and Infrastructure for Supporting Enterprise Knowledge Graphs.- An Interactive Web-based Toolset for Knowledge Discovery from Short Text Log Data.- Carbon: Forecasting Civil Unrest Events by Monitoring News and Social Media.- A system for Querying and Analyzing Urban Regions.- Detect tracking behavior among trajectory data.
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This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos. .
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This two-volume set, LNAI 11012 and 11013, constitutes the thoroughly refereed proceedings of the 15th Pacific Rim Conference on Artificial Intelligence, PRICAI 2018, held in Nanjing, China, in August 2018. The 82 full papers and 58 short papers presented in these volumes were carefully reviewed and selected from 382 submissions. PRICAI covers a wide range of topics such as AI theories, technologies and their applications in the areas of social and economic importance for countries in the Pacific Rim. .
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• Reconstruction of Separable Particle Verbs in a Corpus of Spoken German
• Detecting Vocal Irony
• The Devil is in the Details: Parsing Unknown German Words
• Exploring Ensemble Dependency Parsing to Reduce Manual Annotation Workload
• Different German and English Co-reference Resolution Models for Multi-Domain Content Curation Scenarios
• Word and Sentence Segmentation in German: Overcoming Idiosyncrasies in the Use of Punctuation in Private Communication
• Fine-Grained POS Tagging of German Social Media and Web Texts
• Developing a Stemmer for German Based on a Comparative Analysis of Publicly Available Stemmers
• Negation Modeling for German Polarity Classification
• NECKAr: A Named Entity Classifier for Wikidata
• Investigating the Morphological Complexity of German Named Entities: The Case of the GermEval NER Challenge
• Detecting Named Entities and Relations in German Clinical Reports
• In-Memory Distributed Training of Linear-Chain Conditional Random Fields with an Application to Fine-Grained Named Entity Recognition
• What does this imply? Examining the Impact of Implicitness on the Perception of Hate Speech
• Automatic Classification of Abusive Language and Personal Attacks in Various Forms of Online Communication
• Token Level Code-Switching Detection Using Wikipedia as a Lexical Resource
• How Social Media Text Analysis Can Inform Disaster Management
• A Comparative Study of Uncertainty Based Active Learning Strategies for General Purpose Twitter Sentiment Analysis with Deep Neural Networks
• An Infrastructure for Empowering Internet Users to Handle Fake News and Other Online Media Phenomena
• Different Types of Automated and Semi-automated Semantic Storytelling: Curation Technologies for Different Sectors
• Twitter Geolocation Prediction Using Neural Networks
• Diachronic Variation of Temporal Expressions in Scientific Writing Through the Lens of Relative Entropy
• A Case Study on the Relevance of the Competence Assumption for Implicature Calculation in Dialogue Systems
• Supporting Sustainable Process Documentation
• Optimizing Visual Representations in Semantic Multi-Modal Models with Dimensionality Reduction, De-noising and Contextual Information
• Using Argumentative Structure to Grade Persuasive Essays.

This open access volume constitutes the refereed proceedings of the 27th biennial conference of the German Society for Computational Linguistics and Language Technology, GSCL 2017, held in Berlin, Germany, in September 2017, which focused on language technologies for the digital age. The 16 full papers and 10 short papers included in the proceedings were carefully selected from 36 submissions. Topics covered include text processing of the German language, online media and online content, semantics and reasoning, sentiment analysis, and semantic web description languages.

• Improving Supervised Classification Using Information Extraction
• Supervised Machine Learning Techniques to Detect TimeML Events in French and English
• In Defense of Word Embedding for Generic Text Representation
• Using Distributed Word Representations and mRMR Discriminant Analysis for Multilingual Text Summarization
• Combining Pattern-Based and Distributional Similarity for Graph-Based Noun Categorization
• Acquiring a Large Scale Polarity Lexicon Through Unsupervised Distributional Methods
• Query Refinement Using Conversational Context: A Method and an Evaluation Resource
• Applying Semantic Parsing to Question Answering Over Linked Data: Addressing the Lexical Gap
• Pragmatic Query Answering: Results from a Quantitative Evaluation
• What was the Query? Generating Queries for Document Sets with Applications in Cluster Labeling
• Using Context-Aware and Semantic Similarity Based Model to Enrich Ontology Concepts
• NADIA: A Simplified Approach Towards the Development of Natural Dialogue Systems
• How to Talk to a Cognitive Computer
• Comparing Recursive Autoencoder and Convolutional Network for Phrase-Level Sentiment Polarity Classification
• The Interplay of Language Processing, Reasoning and Decision-Making in Cognitive Computing
• Towards Benevolent Sales Assistants in Retailing Scenarios
• A Rule-Based Approach to Implicit Emotion Detection in Text
• Deciphering Review Comments: Identifying Suggestions, Appreciations and Complaints
• Associating Intent with Sentiment in Weblogs
• PSO-ASent: Feature Selection Using Particle Swarm Optimization for Aspect Based Sentiment Analysis
• Improving Spanish Polarity Classification Combining Different Linguistic Resources
• Tree-Structured Named Entities Extraction from Competing Speech Transcriptions
• Interactive Learning with TREE: Teachable Relation and Event Extraction System
• Identification and Ranking of Event-Specific Entity-Centric Informative Content from Twitter
• Automatic Classification and PLS-PM Modeling for Profiling Reputation of Corporate Entities on Twitter
• An Adaptable and Personalised E-Learning System Based on Free Web Resources
• A Controlled Natural Language for Business Intelligence Monitoring
• Text Summarization and Speech Synthesis for the Automated Generation of Personalized Audio Presentations
• Unsupervised Classification of Translated Texts
• A Language-Independent Method for Detection and Correction of Alignment Errors in Parallel Corpora
• High-Precision Person Name Extraction from Turkish Texts Using Wikipedia
• A Hybrid Approach for Extracting Arabic Persons' Names and Resolving Their Ambiguity from Twitter
• Extracting Relations from Unstructured Text Sources for Music Recommendation
• Simulating Misreading
• (German) Language Processing for Lucene
• Optimized Uyghur Segmentation for Statistical Machine Translation
• Management and Publishing of Multimedia Dictionary of the Czech Sign Language
• Detection of Modality with ITGETARUNS
• Gathering Knowledge for Question Answering Beyond Named Entities
• MaNER: A MedicAl Named Entity Recogniser
• Upper Bound for Cross-Lingual Concept Mapping with External Translation Resources
• Generating Logical Representations for Natural Language Requirements Using Syntactic Dependencies and Norm Analysis Patterns
• Random Indexing Revisited
• On Developing Extraction Rules for Mining Informal Scientific References from Altmetric Data Sources
• Lemonade: A Web Assistant for Creating and Debugging Ontology Lexica
• A Comparative Study on Twitter Sentiment Analysis: Which Features are Good?

This book constitutes the refereed proceedings of the 20th International Conference on Applications of Natural Language to Information Systems, NLDB 2015, held in Passau, Germany, in June 2015. The 18 full papers, 15 short papers, 14 poster and demonstration papers presented were carefully reviewed and selected from 100 submissions. The papers cover the following topics: information extraction, distributional semantics, querying and question answering systems, context-aware NLP, cognitive and semantic computing, sentiment and opinion analysis, information extraction and social media, NLP and usability, text classification and extraction, and posters and demonstrations.

• Compressing Bayesian Networks: Swarm-Based Descent, Efficiency, and Posterior Accuracy.- De-Causalizing NAT-Modeled Bayesian Networks for Inference Efficiency.- A Novel Evaluation Methodology for Assessing Off-Policy Learning Methods in Contextual Bandits.- Synthesizing Controllers: On the Correspondence Between LTL Synthesis and Non-Deterministic Planning.- Logic-Based Benders Decomposition for Two-Stage Flexible Flow Shop Scheduling with Unrelated Parallel Machines.- Advice-Based Exploration in Model-Based Reinforcement Learning.- Deep Super Learner: A Deep Ensemble for Classification Problem.- One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data.- MedFact: Towards Improving Veracity of Medical Information in Social Media Using Applied Machine Learning.- Re-ranking Candidate Lists for Improved Lexical Induction.- Analysis of Social Media Posts for Early Detection of Mental Health Conditions.- Motor Bearing Fault diagnosis Using Deep Convolutional Neural Networks with 2D Analysis of Vibration Signal.- Mobile App for Detection of Counterfeit Banknotes.- A Multi-agent Framework for Understanding Addiction.- Infusing Domain Knowledge to Improve the Detection of Alzheimer's Disease from Everyday Motion Behavior.- An Incremental Machine Learning Algorithm for Nuclear Forensics.- MML-Based Approach for Determining the Number of Topics in EDCM Mixture Models.- Constrained Bayesian Optimization for Problems with Piece-wise Smooth Constraints.- Dimensionality Reduction and Visualization by Doubly Kernelized Unit Ball Embedding.- Accelerated Gradient and Block-wise Gradient Methods for Big Data Factorization.- Learning Belief Revision Operators.- Solving Constraint Satisfaction Problems Using Firey Algorithms.- An AI Planning-Based Approach to the Multi-Agent Plan Recognition Problem.- Predicting Transportation Modes of GPS Trajectories Using Feature Engineering and Noise Removal.- Prediction of Container Damage Insurance Claims for Optimized Maritime Port Operations.- Drug-Target Interaction Network Predictions for Drug Repurposing Using LASSO-based Regularized Linear Classification Model.- Optimal Scheduling for Smart Charging of Electric Vehicles Using Dynamic Programming.- Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Settings.- Text-based Detection of Unauthorized Users of Social Media Accounts.- N-gram Based Approach for Automatic Prediction of Essay Rubric Marks.- Matching Resumes to Job Descriptions with Stacked Models.- Towards a Comprehensive Evaluation of Recommenders: A Cognition-based Approach.- A Sentence-level Sparse Gamma Topic Model for Sentiment Analysis.- Topic Detection and document Similarity on Financial News.- Software Defect Prediction from Code Quality Measurements via Machine Learning.- Automated Scheduling: Reinforcement Learning Approach to Algorithm Policy Learning.- Estimating Vineyard Grape Yield from Images.- Real-time Deep Learning Pedestrians Classification on a Micro-controller.- A Unified Evaluation Framework for Recommender Systems.- Early Detection of Alzheimer's Disease Using Deep Learning.- Learning with Prior Domain Knowledge and Insufficient Annotated Data.- Predicting Crime Using Spatial Features.- A Tool for Defining and Simulating Storage Strategies on the Smart Grid.- Decision Assist for Self-Driving Cars.- Rule Mining and Prediction Using the Flek Machine
• A New Machine Learning Engine.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 31th Canadian Conference on Artificial Intelligence, Canadian AI 2018, held in Toronto, ON, Canada, in May 2018. The 16 regular papers and 18 short papers presented together with 7 Graduate Student Symposium papers and 4 Industry Track papers were carefully reviewed and selected from 72 submissions. The focus of the conference was on artificial intelligence research and advanced information and communications technology.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• CLEF Steering Committee
• Contents
• Full Papers
• A Pinch of Humor for Short-Text Conversation: An Information Retrieval Approach
• 1 Introduction
• 2 Related Work
• 3 Data
• 3.1 Joke Collection
• 3.2 Yahoo!Answers
• 4 Retrieval Models
• 4.1 BM25
• 4.2 Query Term Reweighting Model (QTR)
• 4.3 doc2vec
• 5 Evaluation
• 5.1 Yahoo!Answers
• 5.2 Lab Evaluation
• 6 Conclusion and Future Work
• References
• A Component-Level Analysis of an Academic Search Test Collection.
• 1 Introduction
• 2 Component-Level Evaluation in Academic Search

• 3 Study Configurations
• 3.1 Test Collection and Test IR System
• 3.2 Component-Level Configurations
• 3.3 Analysis Steps
• 4 Analyzing Component Performance
• 4.1 Document and Topic Structure
• 4.2 IR System Components
• 5 Conclusion
• References
• A Component-Level Analysis of an Academic Search Test Collection.
• 1 Introduction
• 2 Query Performance Prediction
• 3 Study Configurations
• 3.1 Test Collection and Test IR System
• 3.2 Analysis Steps
• 4 Analyzing Query Performance
• 4.1 Recall Base
• 4.2 Topic Specificity
• 4.3 Semantic Heterogeneity of Topic Terms

• 5 Conclusion
• References
• Improving the Reliability of Query Expansion for User-Generated Speech Retrieval Using Query Performance Prediction
• 1 Introduction
• 2 Previous Work on QE for Spoken Content
• 3 UGS Test Collection
• 3.1 UGS Document Set
• 3.2 Query and Relevance Sets
• 4 Initial Investigation of QE for UGS Retrieval
• 4.1 Retrieval Model
• 4.2 Query Expansion
• 4.3 Passage-Based QE
• 4.4 Experimental Results
• 5 Query Performance Prediction for QE
• 5.1 Query Performance Prediction Methods
• 5.2 Evaluation
• 6 Adaptive Passage-Based QE for UGS Retrieval

• 7 Conclusions and Further Studies
• References
• Optimized Convolutional Neural Network Ensembles for Medical Subfigure Classification
• 1 Introduction
• 2 Revisiting Participation at ImageCLEF 2016
• 3 Dataset Analysis
• 3.1 Data Generation Process
• 3.2 Problems of the Dataset
• 3.3 Suggestions for Future Datasets
• 4 Methodology
• 5 Evaluation
• 5.1 Results Without Additional Images from ImageCLEF 2013
• 5.2 Self-training Using a PubMedCentral Database Snapshot
• 6 Conclusion
• References
• IRIT-QFR: IRIT Query Feature Resource
• 1 Introduction
• 2 Query Features

• 2.1 Document Collection Independent Pre-retrieval Features: WordNet-Based and Other Linguistic Features
• 2.2 Document Collection Dependent Pre-retrieval Features
• 2.3 Post-retrieval Features
• 2.4 Collections for Which the Features Have Been Estimated
• 3 Analysis of the Resource
• 3.1 Descriptive Analysis
• 4 Applications
• 5 Conclusion
• References
• Evaluating and Improving the Extraction of Mathematical Identifier Definitions
• 1 Introduction
• 2 Related Work
• 2.1 Statistical Feature Analysis (ST)
• 2.2 Pattern Matching (PM)
• 2.3 Machine Learning
• 3 Methodology

This book constitutes the refereed proceedings of the 8th International Conference of the CLEF Initiative, CLEF 2017, held in Dublin, Ireland, in September 2017. The 7 full papers and 9 short papers presented together with 6 best of the labs papers were carefully reviewed and selected from 38 submissions. In addition, this volume contains the results of 10 benchmarking labs reporting their year long activities in overview talks and lab sessions. The papers address all aspects of information access in any modality and language and cover a broad range of topics in the field of multilingual and multimodal information access evaluation.
(source: Nielsen Book Data)

• Introduction.- First approach: sentence analysis using rewriting rules.- Second approach: constituent grammars.- Third approach: dependency trees.- Evaluation of the dependency parser.- Applications.- Prepositional phrase attachment disambiguation.- The unsupervised approach: grammar induction.- Multiple argument handling.- The need for full co-occurrence.
• (source: Nielsen Book Data)

This book describes effective methods for automatically analyzing a sentence, based on the syntactic and semantic characteristics of the elements that form it. To tackle ambiguities, the authors use selectional preferences (SP), which measure how well two words fit together semantically in a sentence. Today, many disciplines require automatic text analysis based on the syntactic and semantic characteristics of language and as such several techniques for parsing sentences have been proposed. Which is better? In this book the authors begin with simple heuristics before moving on to more complex methods that identify nouns and verbs and then aggregate modifiers, and lastly discuss methods that can handle complex subordinate and relative clauses. During this process, several ambiguities arise. SP are commonly determined on the basis of the association between a pair of words. However, in many cases, SP depend on more words. For example, something (such as grass) may be edible, depending on who is eating it (a cow?). Moreover, things such as popcorn are usually eaten at the movies, and not in a restaurant. The authors deal with these phenomena from different points of view.
(source: Nielsen Book Data)