Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers.
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.
However, this approach constrains knowledge sharing across different attributes.
In addition to generic n-gram embeddings (using FastText), we experiment with concatenative (stems) and templatic (roots and patterns) morphological subwords.
We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python.
In this paper we present the first full morphological analysis and disambiguation system for Gulf Arabic.
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.
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.
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.
User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging.
no code implementations • • 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
We make use of the resulting morphological models for scoring and ranking the analyses of the morphological analyzer for morphological disambiguation.
We present Arab-Acquis, a large publicly available dataset for evaluating machine translation between 22 European languages and Arabic.