379 citations · 780 across the 76 of their papers we have counts for
8 papers · 1 filter
A Comparative Analysis of Content-based Geolocation in Blogs and Tweets
Konstantinos Pappas, Mahmoud Azab, Rada Mihalcea
The geolocation of online information is an essential component in any geospatial application. While most of the previous work on geolocation has focused on Twitter, in this paper…
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
Navonil Majumder, Soujanya Poria, Devamanyu Hazarika +3
Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining…
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder +3
Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi…
Speaker Naming in Movies
Mahmoud Azab, Mingzhe Wang, Max Smith +3
We propose a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework. To evaluate the performance of our…
CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla +3
The literature in automated sarcasm detection has mainly focused on lexical, syntactic and semantic-level analysis of text. However, a sarcastic sentence can be expressed with cont…
Multi-Label Transfer Learning for Multi-Relational Semantic Similarity
Li Zhang, Steven R. Wilson, Rada Mihalcea
Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on. Yet, all the systems…
Factors Influencing the Surprising Instability of Word Embeddings
Laura Wendlandt, Jonathan K. Kummerfeld, Rada Mihalcea
Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations. In this paper, we consider one aspec…
Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity
Li Zhang, Steven R. Wilson, Rada Mihalcea
Sentence encoders, which produce sentence embeddings using neural networks, are typically evaluated by how well they transfer to downstream tasks. This includes semantic similarity…