Fine-grained Subjectivity and Sentiment Analysis: Recognizing the intensity, polarity, and attitudes of private states

Private states (mental and emotional states) are part of the information that is conveyed in many forms of discourse. News articles often report emotional responses to news stories; editorials, reviews, and weblogs convey opinions and beliefs. This dissertation investigates the manual and automatic identification of linguistic expressions of private states in a corpus of news documents from the world press. A term for the linguistic expression of private states is subjectivity.The conceptual representation of private states used in this dissertation is that of Wiebe et al. (2005). As part of this research, annotators are trained to identify expressions of private states and their properties, such as the source and the intensity of the private state. This dissertation then extends the conceptual representation of private states to better model the attitudes and targets of private states. The inter-annotator agreement studies conducted for this dissertation show that the various concepts in the original and extended representation of private states can be reliably annotated.Exploring the automatic recognition of various types of private states is also a large part of this dissertation. Experiments are conducted that focus on three types of fine-grained subjectivity analysis: recognizing the intensity of clauses and sentences, recognizing the contextual polarity of words and phrases, and recognizing the attribution levels where sentiment and arguing attitudes are expressed. Various supervised machine learning algorithms are used to train automatic systems to perform each of these tasks. These experiments result in automatic systems for performing fine-grained subjectivity analysis that significantly outperform baseline systems

Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks

Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques. In this paper, we propose a novel deep learning-based approach for this task. Specifically, we present a convolutional neural network (CNN), which performs automatic feature extraction from a given handwritten image, followed by classification of the writer's gender. Also, we introduce a new dataset of labeled handwritten samples, in Hebrew and English, of 405 participants. Comparing the gender classification accuracy on this dataset against human examiners, our results show that the proposed deep learning-based approach is substantially more accurate than that of humans

Automatic gender detection using on-line and off-line information

In this paper, the problem of classifying handwritten data with respect to gender is addressed. A classification method based on Gaussian Mixture Models is applied to distinguish between male and female handwriting. Two sets of features using on-line and off-line information have been used for the classification. Furthermore, we combined both feature sets and investigated several combination strategies. In our experiments, the on-line features produced a higher classification rate than the off-line features. However, the best results were obtained with the combination. The final gender detection rate on the test set is 67.57%, which is significantly higher than the performance of the on-line and off-line system with about 64.25 and 55.39%, respectively. The combined system also shows an improved performance over human-based classification. To the best of the authors' knowledge, the system presented in this paper is the first completely automatic gender detection system which works on on-line data. Furthermore, the combination of on-line and off-line features for gender detection is investigated for the first time in the literatur

Investigating the use of an ensemble of evolutionary algorithms for letter identification in tremulous medieval handwriting

Ensemble classifiers are known for performing good generalization from simpler and less accurate classifiers. Ensembles have the ability to use the variety in classification patterns of the smaller classifiers in order to make better predictions. However, to create an ensemble it is necessary to determine how the component classifiers should be combined to generate the final predictions. One way to do this is to search different combinations of classifiers with evolutionary algorithms, which are largely employed when the objective is to find a structure that serves for some purpose. In this work, an investigation is carried about the use of ensembles obtained via evolutionary algorithm for identifying individual letters in tremulous medieval writing and to differentiate between scribes. The aim of this research is to use this process as the first step towards classifying the tremor type with more accuracy. The ensembles are obtained through evolutionary search of trees that aggregate the output of base classifiers, which are neural networks trained prior to the ensemble search. The misclassification patterns of the base classifiers are analysed in order to determine how much better an ensemble of those classifiers can be than its components. The best ensembles have their misclassification patterns compared to those of their component classifiers. The results obtained suggest interesting methods for letter (up to 96% accuracy) and user classification (up to 88% accuracy) in an offline scenario

Teaching Neural Networks to Detect the Authors of Texts Using Lexical Descriptors

This paper proposes a means of using an artificial neural network to distinguish the authors of paragraphs. Once the network has been trained, its hidden layer activations are recorded as a representation of the average number of words and average characters of words in a paragraphs of an author. This stored information can then be used to identify the texts written by authors. This computational task is solved by dividing it into a number of computationally simple tasks and then combining the solutions to those tasks. Computational simplicity is achieved by distributing the learning task among a number of experts, which in turn divides the input space into a set of subspaces. The combination of these experts is said to constitute a committee machine. Basically, it fuses knowledge acquired by experts to arrive at an overall decision that is supposedly superior to that attainable by anyone of them acting alone. By this, we succeeded to distinguish the paragraphs authored by Ivo Andrić, from the ones authored by Mehmed Meša Selimović

Visual Transfer Learning: Informal Introduction and Literature Overview

Transfer learning techniques are important to handle small training sets and to allow for quick generalization even from only a few examples. The following paper is the introduction as well as the literature overview part of my thesis related to the topic of transfer learning for visual recognition problems.Comment: part of my PhD thesi