LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguation

Al-Hajj, Moustafa; Jarrar, Mustafa

doi:10.18653/v1/2021.semeval-1.99

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…We plan to increase the size of our corpus to cover additional Levantine sub-dialects, especially those of other Levantine areas, most notably some of Syria's dialectal varieties. We also plan to use this corpus to develop morphological analyzers and word-sense disambiguation system for Levantine Arabic as we did for MSA (see (Al-Hajj and Jarrar, 2021a;Al-Hajj and Jarrar, 2021b)). Additionally, we plan to build on the Palestinian and Lebanese dialect lemmas to develop a Levantine-MSA-English Lexicon and extend it with synonyms (Jarrar et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Curras + Baladi: Towards a Levantine Corpus

Haff¹,

Jarrar²,

Hammouda³

et al. 2022

Preprint

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This paper presents two-fold contributions: a full revision of the Palestinian morphologically annotated corpus (Curras), and a newly annotated Lebanese corpus (Baladi). Both corpora can be used as a more general Levantine corpus. Baladi consists of around 9.6K morphologically annotated tokens. Each token was manually annotated with several morphological features and using LDC's SAMA lemmas and tags. The inter-annotator evaluation on most features illustrates 78.5% Kappa and 90.1% F1-Score. Curras was revised by refining all annotations for accuracy, normalization and unification of POS tags, and linking with SAMA lemmas. This revision was also important to ensure that both corpora are compatible and can help to bridge the nuanced linguistic gaps that exist between the two highly mutually intelligible dialects. Both corpora are publicly available through a web portal.

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Section: Discussionmentioning

confidence: 99%

Curras + Baladi: Towards a Levantine Corpus

Haff¹,

Jarrar²,

Hammouda³

et al. 2022

Preprint

Self Cite

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“…A RESTful web service for Arabic NER is developed and deployed online 4 as part of our language understanding resources Al-Hajj and Jarrar, 2021b;Jarrar et al, 2021). The web service takes a text as input and returns the output in three different formats: (i) JSON IOB2, a JSON in which each token in the input text is returned with its corresponding tag similar to the IOB2 scheme, (ii) JSON entities, only the recognized named entities and their positions are returned, and (iii) XML, which is similar to the format (ii), but the named entities are marked up using XML.…”

Section: Methodsmentioning

confidence: 99%

Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT

Jarrar¹,

Khalilia²,

Ghanem³

2022

Preprint

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This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. Wojood consists of about 550K Modern Standard Arabic (MSA) and dialect tokens that are manually annotated with 21 entity types including person, organization, location, event and date. More importantly, the corpus is annotated with nested entities instead of the more common flat annotations. The data contains about 75K entities and 22.5% of which are nested. The inter-annotator evaluation of the corpus demonstrated a strong agreement with Cohen's Kappa of 0.979 and an F1-score of 0.976. To validate our data, we used the corpus to train a nested NER model based on multi-task learning using the pre-trained AraBERT (Arabic BERT). The model achieved an overall micro F1-score of 0.884. Our corpus, the annotation guidelines, the source code and the pre-trained model are publicly available.

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“…We train the Word2Vec model using Indonesian Wikipedia dataset because it is a large dataset that is publicly available. Using the same reason, there are also many previous researchers who also learned their Word2Vec models using Wikipedia dataset [39], [40].…”

Section: Word Embeddingmentioning

confidence: 99%

Enhanced TextRank using weighted word embedding for text summarization

Yulianti,

Pangestu,

Jiwanggi

2023

IJECE

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<p><span lang="EN-US">The length of a news article may influence people’s interest to read the article. In this case, text summarization can help to create a shorter representative version of an article to reduce people’s read time. This paper proposes to use weighted word embedding based on Word2Vec, FastText, and bidirectional encoder representations from transformers (BERT) models to enhance the TextRank summarization algorithm. The use of weighted word embedding is aimed to create better sentence representation, in order to produce more accurate summaries. The results show that using (unweighted) word embedding significantly improves the performance of the TextRank algorithm, with the best performance gained by the summarization system using BERT word embedding. When each word embedding is weighed using term frequency-inverse document frequency (TF-IDF), the performance for all systems using unweighted word embedding further significantly improve, with the biggest improvement achieved by the systems using Word2Vec (with 6.80% to 12.92% increase) and FastText (with 7.04% to 12.78% increase). Overall, our systems using weighted word embedding can outperform the TextRank method by up to 17.33% in ROUGE-1 and 30.01% in ROUGE-2. This demonstrates the effectiveness of weighted word embedding in the TextRank algorithm for text summarization.</span></p>

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LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguation

Cited by 7 publications

References 21 publications

Curras + Baladi: Towards a Levantine Corpus

Curras + Baladi: Towards a Levantine Corpus

Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT

Enhanced TextRank using weighted word embedding for text summarization

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