Commentary and translation in Syriac Aristotelian scholarship: Sergius to Baghdad

This article considers the relationship between the composition in Syriac of commentaries on Aristotle and the translation of his treatises from the time of Sergius of Reshaina through to the Baghdad scholars of the 8th-10th centuries. Surveying the work particularly of Sergius, the scholarly translators of Qenneshre, and the interests of Patriarch Timothy I as evidenced in his letters, it argues that the translation activity up to the 8th century must be seen within the context of a school tradition in which the Syriac text of Aristotle was read in association with a written or oral commentary, or with the Greek text, or both. An appreciation of the link between commentary and translation, as also Syriac and Greek, in Graeco-Syriac Aristotelian scholarship of the 6th-8th centuries enables a better understanding of its relationship to the Syro-Arabic Aristotelian scholarship of Abbasid Baghdad

Automatic Misogyny Detection in Social Media: a Survey

This article presents a survey of automated misogyny identification techniques in social media, especially in Twitter. This problem is urgent because of the high speed at which messages on social platforms grow and the widespread use of offensive language (including misogynistic language) in them. In this article we survey approaches proposed in the literature to solve the problem of misogynistic message recognition. These include classical machine learning models like Sup-port Vector Machine, Naive Bayes, Logistic Regression and ensembles of different classical machine learning models and deep neural networks such as Long Short-term memory and Convolutional Neural Networks. We consider results of experiments with these models in different languages: English, Spanish and Italian tweets. The survey describes some features which help to identify misogynistic tweets and some challenges which aim was to create misogyny language classifiers. The survey includes not only models which help to identify misogyny language, but also systems which help to recognize a target of an offense (an individual or a group of persons)

Extracting Drug-Drug Interactions with Character-Level and Dependency-Based Embeddings

The DDI track of TAC-2018 challenge addresses the problem of an information retrieval of drug-drug interactions on structured product labeling documents with discontinuous and overlapping entities. In this paper, we present our participation for event extraction subtask (Task 1). We used a supervised long-short-term memory (LSTM) network with conditional random fields decoding (LSTM-CRF) approach with an automatic exploring of words and characters features. Additional dependency-based information was integrated into word embeddings to allow better word representation. Our system performed with above median score

Classifying Misogynistic Tweets Using a Blended Model: the AMI Shared Task in IBEREVAL 2018

This article describes a possible solution for Automatic Misogyny Identification (AMI) Shared Task at IBEREVAL-2018. The proposed technique is based on combining several simpler classifiers into one more complex blended model, which classified the data taking into account the probabilities of belonging to classes calculated by simpler models. We used the Logistic Regression, Naive Bayes, and SVM classifiers. The experimental results show that blended model works better than simpler models for all three type of classification, for both binomial classification (Misogyny Identifivation, Target Classification) and multinomial classification (Misogynistic Behavior)

Misogyny Detection and Classification in English Tweets: The Experience of the ITT Team

The problem of online misogyny and women-based offending has become increasingly widespread, and the automatic detection of such messages is an urgent priority. In this paper, we present an approach based on an ensemble of Logistic Regression, Support Vector Machines, and Naïve Bayes models for the detection of misogyny in texts extracted from the Twitter platform. Our method has been presented in the framework of the participation in the Automatic Misogyny Identification (AMI) Shared Task in the EVALITA 2018 evaluation campaign

Adverse drug extraction in twitter data using convolutional neural network

The study of health-related topics on social media has become a useful tool for the early detection of the different adverse medical conditions. In particular, it concerns cases related to the treatment of mental diseases, as the effects of medications here often prove to be unpredictable. In our research, we use convolutional neural networks (CNN) with word2vec embedding to classify user comments on Twitter. The aim of the classification is to reveal adverse drug reactions of users. The results obtained are highly promising, showing the overall usefulness of neural network algorithms in this kind of tasks

Review of trends in health social media analysis

This paper surveys recent publications (2008-2017) on using social media data to study public health. The survey describes the main topics being discussed in forums and presents short information about methods and tools used for analysis health social media. We put especial attention on adverse drug reaction detection problem (ADR)

Comparison of Two-pass Algorithms for Dynamic Topic Modelling Based on Matrix Decompositions

In this paper we present a two-pass algorithm based on different matrix decompositions, such as LSI, PCA, ICA and NMF, which allows tracking of the evolution of topics over time. The proposed dynamic topic models as output give an easily interpreted overview of topics found in a sequentially organized set of documents that does not require further processing. Each topic is presented by a user-specified number of top-terms. Such an approach to topic modeling if applied to, for example, a news article data set, can be convenient and useful for economists, sociologists, political scientists. The proposed approach allows to achieve results comparable to those obtained using complex probabilistic models, such as LDA