no code implementations • 1 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.
no code implementations • EMNLP 2021 • Liqiang Xiao, Jun Ma2, Xin Luna Dong, Pascual Martinez-Gomez, Nasser Zalmout, Wei Chen, Tong Zhao, Hao He, Yaohui Jin
Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers.
no code implementations • 8 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.
no code implementations • ACL 2021 • Jun Yan, Nasser Zalmout, Yan Liang, Christan Grant, Xiang Ren, Xin Luna Dong
However, this approach constrains knowledge sharing across different attributes.
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.
no code implementations • LREC 2020 • Salam Khalifa, Nasser Zalmout, Nizar Habash
In this paper we present the first full morphological analysis and disambiguation system for Gulf Arabic.
1 code implementation • LREC 2020 • Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alex Erdmann, er, Nizar Habash
We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python.
no code implementations • WS 2019 • Nasser Zalmout, Kapil Thadani, Aasish Pappu
This paper presents an approach for detecting and normalizing neologisms in social media content.
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.
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.
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.
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.
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.
no code implementations • LREC 2018 • Nizar Habash, Fadhl Eryani, Salam Khalifa, Owen Rambow, Dana Abdulrahim, Alex Erdmann, er, Reem Faraj, Wajdi Zaghouani, Houda Bouamor, Nasser Zalmout, Sara Hassan, Faisal Al-Shargi, Sakhar Alkhereyf, Basma Abdulkareem, Esk, Ramy er, Mohammad Salameh, Hind Saddiki
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.
no code implementations • EACL 2017 • Nizar Habash, Nasser Zalmout, Dima Taji, Hieu Hoang, Maverick Alzate
We present Arab-Acquis, a large publicly available dataset for evaluating machine translation between 22 European languages and Arabic.
no code implementations • COLING 2016 • Salam Khalifa, Nasser Zalmout, Nizar Habash
In this paper, we present YAMAMA, a multi-dialect Arabic morphological analyzer and disambiguator.
no code implementations • WS 2016 • Nasser Zalmout, Hind Saddiki, Nizar Habash
Much research in education has been done on the study of different language teaching methods.