Alexander F. Gelbukh

Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining

SenticNet 1.0 is one of the most widely used, publicly available resources for concept-based opinion mining. The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label.

Aspect extraction for opinion mining with a deep convolutional neural network

In this paper, we present the first deep learning approach to aspect extraction in opinion mining. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about. We used a 7-layer deep convolutional neural network to tag each word in opinionated sentences as either aspect or non-aspect word. We also developed a set of linguistic patterns for the same purpose and combined them with the neural network. The resulting ensemble classifier, coupled with a word-embedding model for sentiment analysis, allowed our approach to obtain significantly better accuracy than state-of-the-art methods.

Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis

We present a novel way of extracting features from short texts, based on the activation values of an inner layer of a deep convolutional neural network. We use the extracted features in multimodal sentiment analysis of short video clips representing one sentence each. We use the combined feature vectors of textual, visual, and audio modalities to train a classifier based on multiple kernel learning, which is known to be good at heterogeneous data. We obtain 14% performance improvement over the state of the art and present a parallelizable decision-level data fusion method, which is much faster, though slightly less accurate.

Syntactic N-grams as machine learning features for natural language processing

In this paper we introduce and discuss a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner how we construct them, i.e., what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking words as they appear in a text, i.e., sn-grams are constructed by following paths in syntactic trees. In this manner, sn-grams allow bringing syntactic knowledge into machine learning methods; still, previous parsing is necessary for their construction. Sn-grams can be applied in any natural language processing (NLP) task where traditional n-grams are used. We describe how sn-grams were applied to authorship attribution. We used as baseline traditional n-grams of words, part of speech (POS) tags and characters; three classifiers were applied: support vector machines (SVM), naive Bayes (NB), and tree classifier J48. Sn-grams give better results with SVM classifier.

EmoSenticSpace: a novel framework for affective common-sense reasoning

Emotions play a key role in natural language understanding and sensemaking. Pure machine learning usually fails to recognize and interpret emotions in text accurately. The need for knowledge bases that give access to semantics and sentics (the conceptual and affective information) associated with natural language is growing exponentially in the context of big social data analysis. To this end, this paper proposes EmoSenticSpace, a new framework for affective common-sense reasoning that extends WordNet-Affect and SenticNet by providing both emotion labels and polarity scores for a large set of natural language concepts. The framework is built by means of fuzzy c-means clustering and support-vector-machine classification, and takes into account a number of similarity measures, including point-wise mutual information and emotional affinity. EmoSenticSpace was tested on three emotion-related natural language processing tasks, namely sentiment analysis, emotion recognition, and personality detection. In all cases, the proposed framework outperforms the state-of-the-art. In particular, the direct evaluation of EmoSenticSpace against psychological features provided in the benchmark ISEAR dataset shows a 92.15% agreement.

A Rule-Based Approach to Aspect Extraction from Product Reviews

Sentiment analysis is a rapidly growing research field that has attracted both academia and industry because of the challenging research problems it poses and the potential benefits it can provide in many real life applications. Aspect-based opinion mining, in particular, is one of the fundamental challenges within this research field. In this work, we aim to solve the problem of aspect extraction from product reviews by proposing a novel rule-based approach that exploits common-sense knowledge and sentence dependency trees to detect both explicit and implicit aspects. Two popular review datasets were used for evaluating the system against state-of-the-art aspect extraction techniques, obtaining higher detection accuracy for both datasets.

Information Retrieval with Conceptual Graph Matching

The use of conceptual graphs for the representaion of text contents in information retrievel is discussed. A method for measuring the similarity between two texts represented as conceptual graphs is presented. The method is based on well-known strategies of text comparison, such as Dice coefficient, with new elements introduced due to the bipartite nature of the conceptual graphs. Examples of the representation and comparison of the phrases are given. The structure of an information retrieval system using two-level document representation, traditional keywords and conceptual graphs, is presented.

PPChecker: Plagiarism Pattern Checker in Document Copy Detection ∗

Nowadays, most of documents are produced in digital format, in which they can be easily accessed and copied. Document copy detection is a very important tool for protecting the author’s copyright. We present PPChecker, a document copy detection system based on plagiarism pattern checking. PPChecker calculates the amount of data copied from the original document to the query document, based on linguistically-motivated plagiarism patterns. Experiments performed on CISI document collection show that PPChecker produces better decision information for document copy detection than existing systems.

MICAI 2006: Advances in Artificial Intelligence

Artificial Intelligence Arrives to the 21st Century.- Artificial Intelligence Arrives to the 21st Century.- Knowledge Representation and Reasoning.- Properties of Markovian Subgraphs of a Decomposable Graph.- Pre-conceptual Schema: A Conceptual-Graph-Like Knowledge Representation for Requirements Elicitation.- A Recognition-Inference Procedure for a Knowledge Representation Scheme Based on Fuzzy Petri Nets.- Inference Scheme for Order-Sorted Logic Using Noun Phrases with Variables as Sorts.- Answer Set General Theories and Preferences.- A Framework for the E-R Computational Creativity Model.- Fuzzy Logic and Fuzzy Control.- First-Order Interval Type-1 Non-singleton Type-2 TSK Fuzzy Logic Systems.- Fuzzy State Estimation of Discrete Event Systems.- Real-Time Adaptive Fuzzy Motivations for Evolutionary Behavior Learning by a Mobile Robot.- Fuzzy-Based Adaptive Threshold Determining Method for the Interleaved Authentication in Sensor Networks.- A Fuzzy Logic Model for Software Development Effort Estimation at Personal Level.- Reconfigurable Networked Fuzzy Takagi Sugeno Control for Magnetic Levitation Case Study.- Automatic Estimation of the Fusion Method Parameters to Reduce Rule Base of Fuzzy Control Complex Systems.- A Fault Detection System Design for Uncertain T-S Fuzzy Systems.- Uncertainty and Qualitative Reasoning.- An Uncertainty Model for a Diagnostic Expert System Based on Fuzzy Algebras of Strict Monotonic Operations.- A Connectionist Fuzzy Case-Based Reasoning Model.- Error Bounds Between Marginal Probabilities and Beliefs of Loopy Belief Propagation Algorithm.- Applications of Gibbs Measure Theory to Loopy Belief Propagation Algorithm.- A Contingency Analysis of LeActiveMaths Learner Model.- Constructing Virtual Sensors Using Probabilistic Reasoning.- Solving Hybrid Markov Decision Processes.- Comparing Fuzzy Naive Bayes and Gaussian Naive Bayes for Decision Making in RoboCup 3D.- Using the Beliefs of Self-Efficacy to Improve the Effectiveness of ITS: An Empirical Study.- Qualitative Reasoning and Bifurcations in Dynamic Systems.- Evolutionary Algorithms and Swarm Intelligence.- Introducing Partitioning Training Set Strategy to Intrinsic Incremental Evolution.- Evolutionary Method for Nonlinear Systems of Equations.- A Multi-objective Particle Swarm Optimizer Hybridized with Scatter Search.- Neural Networks.- An Interval Approach for Weights Initialization of Feedforward Neural Networks.- Aggregating Regressive Estimators: Gradient-Based Neural Network Ensemble.- The Adaptive Learning Rates of Extended Kalman Filter Based Training Algorithm for Wavelet Neural Networks.- Multistage Neural Network Metalearning with Application to Foreign Exchange Rates Forecasting.- Genetic Optimizations for Radial Basis Function and General Regression Neural Networks.- Complexity of Alpha-Beta Bidirectional Associative Memories.- A New Bi-directional Associative Memory.- Optimization and Scheduling.- A Hybrid Ant Algorithm for the Airline Crew Pairing Problem.- A Refined Evaluation Function for the MinLA Problem.- ILS-Perturbation Based on Local Optima Structure for the QAP Problem.- Application of Fuzzy Multi-objective Programming Approach to Supply Chain Distribution Network Design Problem.- Route Selection and Rate Allocation Using Evolutionary Computation Algorithms in Multirate Multicast Networks.- A Polynomial Algorithm for 2-Cyclic Robotic Scheduling.- A New Algorithm That Obtains an Approximation of the Critical Path in the Job Shop Scheduling Problem.- A Quay Crane Scheduling Method Considering Interference of Yard Cranes in Container Terminals.- Comparing Schedule Generation Schemes in Memetic Algorithms for the Job Shop Scheduling Problem with Sequence Dependent Setup Times.- A Fuzzy Set Approach for Evaluating the Achievability of an Output Time Forecast in a Wafer Fabrication Plant.- Machine Learning and Feature Selection.- How Good Are the Bayesian Information Criterion and the Minimum Description Length Principle for Model Selection? A Bayesian Network Analysis.- Prediction of Silkworm Cocoon Yield in China Based on Grey-Markov Forecasting Model.- A Novel Hybrid System with Neural Networks and Hidden Markov Models in Fault Diagnosis.- Power System Database Feature Selection Using a Relaxed Perceptron Paradigm.- Feature Elimination Approach Based on Random Forest for Cancer Diagnosis.- On Combining Fractal Dimension with GA for Feature Subset Selecting.- Locally Adaptive Nonlinear Dimensionality Reduction.- Classification.- Fuzzy Pairwise Multiclass Support Vector Machines.- Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets.- Optimizing Weighted Kernel Function for Support Vector Machine by Genetic Algorithm.- Decision Forests with Oblique Decision Trees.- Using Reliable Short Rules to Avoid Unnecessary Tests in Decision Trees.- Selection of the Optimal Wavebands for the Variety Discrimination of Chinese Cabbage Seed.- Hybrid Method for Detecting Masqueraders Using Session Folding and Hidden Markov Models.- Toward Lightweight Detection and Visualization for Denial of Service Attacks.- Tri-training and Data Editing Based Semi-supervised Clustering Algorithm.- Knowledge Discovery.- Automatic Construction of Bayesian Network Structures by Means of a Concurrent Search Mechanism.- Collaborative Design Optimization Based on Knowledge Discovery from Simulation.- Behavioural Proximity Approach for Alarm Correlation in Telecommunication Networks.- The MineSP Operator for Mining Sequential Patterns in Inductive Databases.- Visual Exploratory Data Analysis of Traffic Volume.- Computer Vision.- A Fast Model-Based Vision System for a Robot Soccer Team.- Statistics of Visual and Partial Depth Data for Mobile Robot Environment Modeling.- Automatic Facial Expression Recognition with AAM-Based Feature Extraction and SVM Classifier.- Principal Component Net Analysis for Face Recognition.- Advanced Soft Remote Control System Using Hand Gesture.- IMM Method Using Tracking Filter with Fuzzy Gain.- Image Processing and Image Retrieval.- Complete FPGA Implemented Evolvable Image Filters.- Probabilistic Rules for Automatic Texture Segmentation.- A Hybrid Segmentation Method Applied to Color Images and 3D Information.- Segmentation of Medical Images by Using Wavelet Transform and Incremental Self-Organizing Map.- Optimal Sampling for Feature Extraction in Iris Recognition Systems.- Histograms, Wavelets and Neural Networks Applied to Image Retrieval.- Adaptive-Tangent Space Representation for Image Retrieval Based on Kansei.- Natural Language Processing.- Distributions of Functional and Content Words Differ Radically.- Speeding Up Target-Language Driven Part-of-Speech Tagger Training for Machine Translation.- Defining Classifier Regions for WSD Ensembles Using Word Space Features.- Impact of Feature Selection for Corpus-Based WSD in Turkish.- Spanish All-Words Semantic Class Disambiguation Using Cast3LB Corpus.- An Approach for Textual Entailment Recognition Based on Stacking and Voting.- Textual Entailment Beyond Semantic Similarity Information.- On the Identification of Temporal Clauses.- Issues in Translating from Natural Language to SQL in a Domain-Independent Natural Language Interface to Databases.- Information Retrieval and Text Classification.- Interlinguas: A Classical Approach for the Semantic Web. A Practical Case.- A Fuzzy Embedded GA for Information Retrieving from Related Data Set.- On Musical Performances Identification, Entropy and String Matching.- Adaptive Topical Web Crawling for Domain-Specific Resource Discovery Guided by Link-Context.- Evaluating Subjective Compositions by the Cooperation Between Human and Adaptive Agents.- Using Syntactic Distributional Patterns for Data-Driven Answer Extraction from the Web.- Applying NLP Techniques and Biomedical Resources to Medical Questions in QA Performance.- Fast Text Categorization Based on a Novel Class Space Model.- A High Performance Prototype System for Chinese Text Categorization.- A Bayesian Approach to Classify Conference Papers.- An Ontology Based for Drilling Report Classification.- Topic Selection of Web Documents Using Specific Domain Ontology.- Speech Processing.- Speech Recognition Using Energy, MFCCs and Rho Parameters to Classify Syllables in the Spanish Language.- Robust Text-Independent Speaker Identification Using Hybrid PCA&LDA.- Hybrid Algorithm Applied to Feature Selection for Speaker Authentication.- Using PCA to Improve the Generation of Speech Keys.- Multiagent Systems.- Verifying Real-Time Temporal, Cooperation and Epistemic Properties for Uncertain Agents.- Regulating Social Exchanges Between Personality-Based Non-transparent Agents.- Using MAS Technologies for Intelligent Organizations: A Report of Bottom-Up Results.- Modeling and Simulation of Mobile Agents Systems Using a Multi-level Net Formalism.- Using AI Techniques for Fault Localization in Component-Oriented Software Systems.- Robotics.- Exploring Unknown Environments with Randomized Strategies.- Integration of Evolution with a Robot Action Selection Model.- A Hardware Architecture Designed to Implement the GFM Paradigm.- Bioinformatics and Medical Applications.- Fast Protein Structure Alignment Algorithm Based on Local Geometric Similarity.- Robust EMG Pattern Recognition to Muscular Fatigue Effect for Human-Machine Interaction.- Classification of Individual and Clustered Microcalcifications in Digital Mammograms Using Evolutionary Neural Networks.- Heart Cavity Detection in Ultrasound Images with SOM.- An Effective Method of Gait Stability Analysis Using Inertial Sensors.

Flexible Comparison of Conceptual Graphs

Conceptual graphs allow for powerful and computationally affordable representation of the semantic contents of natural language texts. We propose a method of comparison (approximate matching) of conceptual graphs. The method takes into account synonymy and subtype/supertype relationships between the concepts and relations used in the conceptual graphs, thus allowing for greater flexibility of approximate matching. The method also allows the user to choose the desirable aspect of similarity in the cases when the two graphs can be generalized in different ways. The algorithm and examples of its application are presented. The results are potentially useful in a range of tasks requiring approximate semantic or another structural matching - among them, information retrieval and text mining.