Search Results for author:
Found 5 papers, 1 papers with code
Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text
This paper proposes a new state-of-the-art deep learning architecture that uses a novel Bidirectional Inter-Sentence Contextual Attention mechanism (Bi-ISCA) to capture inter-sentence dependencies for detecting sarcasm in the user-generated short text using only the conversational context.
Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention
The topical stance detection problem addresses detecting the stance of the text content with respect to a given topic: whether the sentiment of the given text content is in FAVOR of (positive), is AGAINST (negative), or is NONE (neutral) towards the given topic.
EmTaggeR: A Word Embedding Based Novel Method for Hashtag Recommendation on Twitter
The top-K hashtags that appear in this ranked list, are recommended for the given test post.
A Paraphrase and Semantic Similarity Detection System for User Generated Short-Text Content on Microblogs
We propose a set of features that, although well-known in the NLP literature for solving other problems, have not been explored for detecting paraphrase or semantic similarity, on noisy user-generated short-text data such as Twitter.
