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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 – 5 of 5 1 AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding ... 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; Dong, lunadong@amazon.com; Grant, Christan. - : Underline Science Inc., 2021 BASE Show details 2 Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling ... Zalmout, Nasser; Habash, Nizar. - : arXiv, 2019 BASE Show details 3 Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging ... Zalmout, Nasser; Habash, Nizar. - : arXiv, 2019 Abstract: Semitic languages can be highly ambiguous, having several interpretations of the same surface forms, and morphologically rich, having many morphemes that realize several morphological features. This is further exacerbated for dialectal content, which is more prone to noise and lacks a standard orthography. The morphological features can be lexicalized, like lemmas and diacritized forms, or non-lexicalized, like gender, number, and part-of-speech tags, among others. Joint modeling of the lexicalized and non-lexicalized features can identify more intricate morphological patterns, which provide better context modeling, and further disambiguate ambiguous lexical choices. However, the different modeling granularity can make joint modeling more difficult. Our approach models the different features jointly, whether lexicalized (on the character-level), where we also model surface form normalization, or non-lexicalized (on the word-level). We use Arabic as a test case, and achieve state-of-the-art results for Modern ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1910.02267 https://arxiv.org/abs/1910.02267 BASE Hide details 4 Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models ... Watson, Daniel; Zalmout, Nasser; Habash, Nizar. - : arXiv, 2018 BASE Show details 5 Optimizing Tokenization Choice for Machine Translation across Multiple Target Languages Zalmout Nasser; Habash Nizar In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 257-269 (2017) (2017) BASE Show details

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