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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 11 of 11 1 Text mining at multiple granularity: leveraging subwords, words, phrases, and sentences El-Kishky, Ahmed Hassan. - 2022 BASE Show details 2 Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications ... Sun, Shuo; El-Kishky, Ahmed; Chaudhary, Vishrav. - : arXiv, 2021 BASE Show details 3 As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Cohn, Trevor; El-Kishky, Ahmed. - : Underline Science Inc., 2021 BASE Show details 4 Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Cohn, Trevor; El-Kishky, Ahmed. - : Underline Science Inc., 2021 BASE Show details 5 XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment ... El-Kishky, Ahmed; Renduchintala, Adithya; Cross, James. - : arXiv, 2021 BASE Show details 6 Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ... Ko, Wei-Jen; El-Kishky, Ahmed; Renduchintala, Adithya. - : arXiv, 2021 BASE Show details 7 XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; El-Kishky, Ahmed. - : Underline Science Inc., 2021 BASE Show details 8 Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover's Distance ... El-Kishky, Ahmed; Guzmán, Francisco. - : arXiv, 2020 BASE Show details 9 Beyond English-Centric Multilingual Machine Translation ... Fan, Angela; Bhosale, Shruti; Schwenk, Holger. - : arXiv, 2020 BASE Show details 10 An exploratory study on multilingual quality estimation Renduchintala, Adithya; Fomicheva, Marina; Blain, Frederic... In: 366 ; 377 (2020) BASE Show details 11 Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks Wang, Chenguang; Song, Yangqiu; El-Kishky, Ahmed; Roth, Dan; Zhang, Ming; Han, Jiawei. - 2015 Abstract: One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this paper, we provide an example of using world knowledge for domain dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, Word-Net. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features. Keyword: Article URL: https://doi.org/10.1145/2783258.2783374 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4688021/ BASE Hide details

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