this research, relying on critical discourse analysis(cda), has proceed to analyze ten novels of modern iranian men and women writers.in the field of lexicon, using marked lexicon and metaphor, and in the section of syntax, using syntactic non – norm in the texts, it has compared active vs. passive sentences, long vs. short sentences, and direct vs. indirect speech with regard to gender of the ...

Lexicon-based classifier is in the long term one of the main and most effective methods of polarity classification used in sentiment analysis, i.e. computational study of opinions, sentiments and emotions expressed in text (see Liu, 2010). Although it achieves relatively good results also for Czech, the classifier still shows some error rate. This paper provides a detailed analysis of such erro...

Chemicals as therapeutics and investigational agents receive much attention in clinical research and applications recently. However, automated approaches to recognize and categorize the chemical entities in biomedical text are challenging because of the wide varieties of morphologies and nomenclature. We present here a hybrid text mining system that combines chemical lexicon and patterns for re...

This paper presents two experiments with real world applications of word sense disambiguation, wordnets and dependency parsing. The first is an effort towards a portuguese wordnet annotated corpus. We manually annotated 30 sentences using OpenWordNet-PT as a lexicon and then compared the results with an automatic annotation. In addition to the system’s evaluation, the results provided valuable ...

Information and news about Covid-19 received various responses from social media users, including Twitter users. Changes in netizen opinion time to are interesting analyze, especially the patterns of public sentiment emotions contained these opinions. Sentiment emotional conditions can illustrate public's response pandemic Indonesia. This research has two objectives, first reveal types that eme...

Given a controversial issue, argument mining from texts in natural language is extremely challenging: besides linguistic aspects, domain knowledge is often required together with appropriate forms of inferences to identify arguments. A major challenge is then to organize the arguments which have been mined to generate a synthesis that is relevant and usable. We show that the Generative Lexicon ...

Extracting textual data from Greek corpuses poses additional difficulties than in English texts as inclinations and intonation differentiate terms of equal information weight. Pre-processing and normalization of text is an important step before the extraction procedure as it leads to fewer rules and lexicon entries, thus to less execution time and greater success of the mining process. This pap...

This paper describes the system we developed for PSB 2016 social media mining shared task on binary classification of adverse drug reactions (ADRs). The task focuses on automatic classification of ADR assertive user posts. We propose a weighted average ensemble of four classifiers: (1) a concept-matching classifier based on ADR lexicon; (2) a maximum entropy (ME) classifier with word-level n-gr...

We introduce an error mining technique for automatically detecting errors in resources that are used in parsing systems. We applied this technique to parsing results produced on several million words by two distinct parsing systems, which share the syntactic lexicon and the pre-parsing processing chain. We are thus able to identify incorrectness and incompleteness sources in the resources. In p...

This paper introduces a novel deep learning framework including a lexicon-based approach for sentencelevel prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory netwo...