Search Results for author: Nasser Zalmout

Found 19 papers, 1 papers with code

PV2TEA: Patching Visual Modality to Textual-Established Information Extraction

no code implementations1 Jun 2023 Hejie Cui, Rongmei Lin, Nasser Zalmout, Chenwei Zhang, Jingbo Shang, Carl Yang, Xian Li

Information extraction, e. g., attribute value extraction, has been extensively studied and formulated based only on text.

Attribute Attribute Value Extraction

PAM: Understanding Product Images in Cross Product Category Attribute Extraction

no code implementations8 Jun 2021 Rongmei Lin, Xiang He, Jie Feng, Nasser Zalmout, Yan Liang, Li Xiong, Xin Luna Dong

Understanding product attributes plays an important role in improving online shopping experience for customers and serves as an integral part for constructing a product knowledge graph.

Attribute Attribute Extraction +4

Utilizing Subword Entities in Character-Level Sequence-to-Sequence Lemmatization Models

no code implementations COLING 2020 Nasser Zalmout, Nizar Habash

In addition to generic n-gram embeddings (using FastText), we experiment with concatenative (stems) and templatic (roots and patterns) morphological subwords.

LEMMA Lemmatization

Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling

no code implementations ACL 2019 Nasser Zalmout, Nizar Habash

In this paper we explore the use of multitask learning and adversarial training to address morphological richness and dialectal variations in the context of full morphological tagging.

Morphological Tagging Transfer Learning

Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging

no code implementations ACL 2020 Nasser Zalmout, Nizar Habash

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.

Lemmatization Morphological Tagging

Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models

no code implementations EMNLP 2018 Daniel Watson, Nasser Zalmout, Nizar Habash

We show that providing the model with word-level features bridges the gap for the neural network approach to achieve a state-of-the-art F1 score on a standard Arabic language correction shared task dataset.

Word Embeddings

Addressing Noise in Multidialectal Word Embeddings

no code implementations ACL 2018 Alex Erdmann, er, Nasser Zalmout, Nizar Habash

Arabic dialects lack large corpora and are noisy, being linguistically disparate with no standardized spelling.

Sentence Transliteration +1

Noise-Robust Morphological Disambiguation for Dialectal Arabic

no code implementations NAACL 2018 Nasser Zalmout, Alex Erdmann, er, Nizar Habash

User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging.

Lexical Normalization Morphological Analysis +3

Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic

no code implementations EMNLP 2017 Nasser Zalmout, Nizar Habash

We make use of the resulting morphological models for scoring and ranking the analyses of the morphological analyzer for morphological disambiguation.

Feature Engineering Language Modelling +3

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