Classification of Radiology Reports Using Neural Attention Models

The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value. Distinguished from previous studies, our approach embraces a double-annotated dataset and strays away from obscure "black-box" models to comprehensive deep learning models. In this paper, we present a novel neural attention mechanism that not only classifies clinically important findings. Specifically, convolutional neural networks (CNN) with attention analysis are used to classify radiology head computed tomography reports based on five categories that radiologists would account for in assessing acute and communicable findings in daily practice. The experiments show that our CNN attention models outperform non-neural models, especially when trained on a larger dataset. Our attention analysis demonstrates the intuition behind the classifier's decision by generating a heatmap that highlights attended terms used by the CNN model; this is valuable when potential downstream medical decisions are to be performed by human experts or the classifier information is to be used in cohort construction such as for epidemiological studies

Improving Sentiment Analysis in Arabic Using Word Representation

The complexities of Arabic language in morphology, orthography and dialects makes sentiment analysis for Arabic more challenging. Also, text feature extraction from short messages like tweets, in order to gauge the sentiment, makes this task even more difficult. In recent years, deep neural networks were often employed and showed very good results in sentiment classification and natural language processing applications. Word embedding, or word distributing approach, is a current and powerful tool to capture together the closest words from a contextual text. In this paper, we describe how we construct Word2Vec models from a large Arabic corpus obtained from ten newspapers in different Arab countries. By applying different machine learning algorithms and convolutional neural networks with different text feature selections, we report improved accuracy of sentiment classification (91%-95%) on our publicly available Arabic language health sentiment dataset [1]Comment: Authors accepted version of submission for ASAR 201

A Fair and Comprehensive Comparison of Multimodal Tweet Sentiment Analysis Methods

Opinion and sentiment analysis is a vital task to characterize subjective information in social media posts. In this paper, we present a comprehensive experimental evaluation and comparison with six state-of-the-art methods, from which we have re-implemented one of them. In addition, we investigate different textual and visual feature embeddings that cover different aspects of the content, as well as the recently introduced multimodal CLIP embeddings. Experimental results are presented for two different publicly available benchmark datasets of tweets and corresponding images. In contrast to the evaluation methodology of previous work, we introduce a reproducible and fair evaluation scheme to make results comparable. Finally, we conduct an error analysis to outline the limitations of the methods and possibilities for the future work.Comment: Accepted in Workshop on Multi-ModalPre-Training for Multimedia Understanding (MMPT 2021), co-located with ICMR 202

De los lexicones: NLP en la construcción del Lexicón de Drivers de Mercado en Español

In our approach when analyzing and interpreting financial news, we state that apart from the need of a tailored-made lexicon it is critical to design market drivers lexicons, defining a “driver” as the factor or force that has a material impact on a specific activity on another entity, which is contextually dependent and which affects the financial market at a specific time. Therefore, our proposal contemplates three key aspects: first, the conceptualization of the market driver typology, second, a brief explanation of Natural Language Processing (NLP) techniques applied in the construction of market drivers lexicons to finally explain the relevance of the market drivers lexicons in the interpretation of financial news and its correlation to the market movements.En nuestra aproximación sobre el análisis e interpretación de las noticias financieras sostenemos que, además de la necesidad de un lexicón de propósitos específicos para finanzas, es fundamental contar con lexicones de “drivers de mercado”, siendo un driver de mercado, aquel factor que ejerce un efecto material sobre una actividad de otra entidad, contextualmente dependiente y que afecta al mercado financiero en un momento determinado. Desde nuestro enfoque, proponemos: en primer lugar, conceptualizar las diferentes categorías de “drivers de mercado”, en segundo lugar, explicar de manera sucinta cómo mediante las técnicas de Procesamiento de Lenguaje Natural (NLP) se realiza la construcción del lexicón de drivers, y explicitar la relevancia del lexicón de drivers en la interpretación de noticias financieras y su correlación con los movimientos del mercado