Moshe Wasserblat, Intel

Moshe Wasserblat, Intel

Moshe Wasserblat is the Natural Language Processing and Deep Learning Research Group manager for Intel’s Artificial Intelligence Products Group. Previously, he was with NICE Systems for more than 17 years, where he founded and led the speech and text analytics research team. His interests are in the field of speech processing and natural language processing. He was the co-founder and coordinator of the EXCITEMENT FP7 ICT program, has filed more than 60 patents in the field of language technology and has several publications in international conferences and journals.

Weakly-Supervised Aspect-Based Sentiment Extraction

Aspect Based Sentiment Analysis (ABSA) is the task of extracting aspect terms and their related sentiment polarity. This fine-grained trait of ABSA makes it an effective application for organizations to monitor the ratio of positive to negative sentiment expressed towards specific aspects of a product or service, and extract valuable targeted insight to understand customer opinions on products and services, enabling the ability to react accordingly.

However, the deployment of ABSA applications in commercial environments faces major challenges – from scaling across different domains to improving focused learning from a small set of examples.

In this session, we will present ABSApp, a portable system for weakly-supervised aspect-based sentiment extraction. The system is interpretable and user-friendly and does not require labeled training data, hence can be rapidly and cost-effectively used across different domains in applied setups.

ABSApp has been successfully used in a number of real-life use cases, among them movie review analysis and convention impact analysis, and is offered by MS to demonstrate best NLP practices using Azure.


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