SimVecs: Similarity-Based Vectors for Utterance Representation in Conversational AI Systems
Ashraf Mahgoub | Youssef Shahin | Riham Mansour | Saurabh Bagchi
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Conversational AI systems are gaining a lot of attention recently in both industrial and scientific domains, providing a natural way of interaction between customers and adaptive intelligent systems. A key requirement in these systems is the ability to understand the user’s intent and provide adequate responses to them. One of the greatest challenges of language understanding (LU) services is efficient utterance (sentence) representation in vector space, which is an essential step for most ML tasks. In this paper, we propose a novel approach for generating vector space representations of utterances using pair-wise similarity metrics. The proposed approach uses only a few corpora to tune the weights of the similarity metric without relying on external general purpose ontologies. Our experiments confirm that the generated vectors can improve the performance of LU services in unsupervised, semi-supervised and supervised learning tasks.
Augmenting Business Entities with Salient Terms from Twitter
Riham Mansour | Nesma Refaei | Vanessa Murdock
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers
Revisiting the Old Kitchen Sink: Do we Need Sentiment Domain Adaptation?
Riham Mansour | Nesma Refaei | Michael Gamon | Ahmed Abdul-Hamid | Khaled Sami
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013
- Nesma Refaei 2
- Ashraf Mahgoub 1
- Youssef Shahin 1
- Saurabh Bagchi 1
- Vanessa Murdock 1
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