Imran Sheikh


2018

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Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities
Imran Sheikh | Sri Harsha Dumpala | Rupayan Chakraborty | Sunil Kumar Kopparapu
Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML)

Multimodal sentiment classification in practical applications may have to rely on erroneous and imperfect views, namely (a) language transcription from a speech recognizer and (b) under-performing acoustic views. This work focuses on improving the representations of these views by performing a deep canonical correlation analysis with the representations of the better performing manual transcription view. Enhanced representations of the imperfect views can be obtained even in absence of the perfect views and give an improved performance during test conditions. Evaluations on the CMU-MOSI and CMU-MOSEI datasets demonstrate the effectiveness of the proposed approach.

2016

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How Diachronic Text Corpora Affect Context based Retrieval of OOV Proper Names for Audio News
Imran Sheikh | Irina Illina | Dominique Fohr
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Out-Of-Vocabulary (OOV) words missed by Large Vocabulary Continuous Speech Recognition (LVCSR) systems can be recovered with the help of topic and semantic context of the OOV words captured from a diachronic text corpus. In this paper we investigate how the choice of documents for the diachronic text corpora affects the retrieval of OOV Proper Names (PNs) relevant to an audio document. We first present our diachronic French broadcast news datasets, which highlight the motivation of our study on OOV PNs. Then the effect of using diachronic text data from different sources and a different time span is analysed. With OOV PN retrieval experiments on French broadcast news videos, we conclude that a diachronic corpus with text from different sources leads to better retrieval performance than one relying on text from single source or from a longer time span.

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Learning Word Importance with the Neural Bag-of-Words Model
Imran Sheikh | Irina Illina | Dominique Fohr | Georges Linarès
Proceedings of the 1st Workshop on Representation Learning for NLP