no code implementations • 2 Jun 2021 • Zhe Liu, Yufan Guo, Jalal Mahmud
Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment.
no code implementations • NAACL 2021 • Zhe Liu, Yufan Guo, Jalal Mahmud
Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment.
no code implementations • NAACL (TrustNLP) 2021 • Amita Misra, Zhe Liu, Jalal Mahmud
Customers of machine learning systems demand accountability from the companies employing these algorithms for various prediction tasks.
no code implementations • 25 Sep 2019 • Yash Bhalgat, Zhe Liu, Pritam Gundecha, Jalal Mahmud, Amita Misra
Given that labeled data is expensive to obtain in real-world scenarios, many semi-supervised algorithms have explored the task of exploitation of unlabeled data.
no code implementations • WS 2019 • Amita Misra, Mansurul Bhuiyan, Jalal Mahmud, Saurabh Tripathy
We further investigate the results of negation scope detection for the sentiment prediction task on customer service conversation data using both a traditional SVM and a Neural Network.
no code implementations • 12 Nov 2018 • Rama Akkiraju, Vibha Sinha, Anbang Xu, Jalal Mahmud, Pritam Gundecha, Zhe Liu, Xiaotong Liu, John Schumacher
For example, existing machine learning processes cannot address how to define business use cases for an AI application, how to convert business requirements from offering managers into data requirements for data scientists, and how to continuously improve AI applications in term of accuracy and fairness, and how to customize general purpose machine learning models with industry, domain, and use case specific data to make them more accurate for specific situations etc.
no code implementations • 16 Jul 2018 • Mansurul Bhuiyan, Amita Misra, Saurabh Tripathy, Jalal Mahmud, Rama Akkiraju
Lately, there have been several works proposing a novel taxonomy for fine-grained dialogue acts as well as develop algorithms for automatic detection of these acts.
no code implementations • 15 Sep 2017 • Shereen Oraby, Pritam Gundecha, Jalal Mahmud, Mansurul Bhuiyan, Rama Akkiraju
We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries.
no code implementations • 18 Apr 2017 • Pierre-Hadrien Arnoux, Anbang Xu, Neil Boyette, Jalal Mahmud, Rama Akkiraju, Vibha Sinha
Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media.
no code implementations • 17 Mar 2017 • Zhe Liu, Anbang Xu, Mengdi Zhang, Jalal Mahmud, Vibha Sinha
One problem that every presenter faces when delivering a public discourse is how to hold the listeners' attentions or to keep them involved.
no code implementations • 7 Mar 2014 • Jalal Mahmud, Jeffrey Nichols, Clemens Drews
We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone or geographic region, using the content of users tweets and their tweeting behavior.
no code implementations • 26 Feb 2014 • Jalal Mahmud, Jilin Chen, Jeffrey Nichols
We present a study to analyze how word use can predict social engagement behaviors such as replies and retweets in Twitter.