Detect and Classify -- Joint Span Detection and Classification for Health Outcomes

A health outcome is a measurement or an observation used to capture and assess the effect of a treatment. Automatic detection of health outcomes from text would undoubtedly speed up access to evidence necessary in healthcare decision making. Prior work on outcome detection has modelled this task as either (a) a sequence labelling task, where the goal is to detect which text spans describe health outcomes, or (b) a classification task, where the goal is to classify a text into a pre-defined set of categories depending on an outcome that is mentioned somewhere in that text. However, this decoupling of span detection and classification is problematic from a modelling perspective and ignores global structural correspondences between sentence-level and word-level information present in a given text. To address this, we propose a method that uses both word-level and sentence-level information to simultaneously perform outcome span detection and outcome type classification. In addition to injecting contextual information to hidden vectors, we use label attention to appropriately weight both word and sentence level information. Experimental results on several benchmark datasets for health outcome detection show that our proposed method consistently outperforms decoupled methods, reporting competitive results

Text annotation using textual semantic similarity and term-frequency (Twitter)

Researchers on social-media understandably assert that the contributions social media has made on various sectors is massive. Business development managers today have directed a huge amount of effort in strategizing efficient collaboration with both customers and other organizations using social-media. Despite the visible impact social media has made, a lot of digitally shared information is yet to be revealed. Gradually twitter has become the main hub for many Information system researchers, because tweets can freely be accessible in real-time by any one. Motivated by earlier studies where IS researchers addressed big-data analysis and management by employing content analysis techniques, this paper proposes a novel approach to perform unsupervised classification of the tweets into different labels. It introduces a unique algorithm that uses semantic similarity between texts, Term-frequency and a determinant threshold to perform content analysis. The goal of this approach is to extract relevant features from a tweet thus reducing dimension and preparing training datasets that would be used to build classifiers

Text Annotation using Textual Semantic Similarity and Term-Frequency (Twitter).

Researchers on social-media understandably assert that the contributions social media has made on various sectors is massive. Business development managers today have directed a huge amount of effort in strategizing efficient collaboration with both customers and other organizations using social-media. Despite the visible impact social media has made, a lot of digitally shared information is yet to be revealed. Gradually twitter has become the main hub for many Information system researchers because tweets can freely be accessible in real-time by anyone. Motivated by earlier studies where IS researchers addressed big-data analysis and management by employing content analysis techniques, this paper proposes a novel approach to perform unsupervised classification of the tweets into different labels. It introduces a unique algorithm that uses semantic similarity between texts, Term-frequency and a determinant threshold to perform content analysis. The goal of this approach to extract relevant features from a tweet thus reducing dimension and preparing training datasets that would be used to build classifiers