RDI_Team at SemEval-2016 Task 3: RDI Unsupervised Framework for Text Ranking

Magooda, Ahmed; Gomaa, Amr; Mahgoub, Ashraf; Ahmed, Hany; Rashwan, Mohsen; Raafat, Hazem M.; Kamal, Eslam; Sallab, Ahmad A. Al

doi:10.18653/v1/s16-1127

Cited by 6 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A. Magooda et al [2] proposed a ranking system of three components: TF-IDF based module, Language model (LM) based module, and Wikipedia-based module. Then, the three relevancy values calculated are then converted into one relevancy score using weighted summation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The ranking task is reordering the results retrieved from the search tool based on the relevancy between the search result and the original inquiry issued. It is a central task in many NLP topics like information retrieval, question answering, disambiguation, text summarization, plagiarism detection, paraphrase identification, and machine translation [2]. Finding the similarity between terms is the essential portion of textual similarity, then used as a major phase for sentence-level, paragraph-level, and script-level similarities.…”

Section: Introductionmentioning

confidence: 99%

“…The differences in the approaches that exist in the literature review for textual similarity depend on the text representation scheme used before text comparison. There are different text representation schemes suggested by researchers likes Term Frequency-Inverse Document Frequency (TF-IDF) 1 , Latent Semantic Indexing (LSI) 2 , and Graph-based Representation [3], [4], [5]. Due to these ways, the similarity measure to compare text units also differs because one similarity measure may not be convenient for all representation schemes.…”

Section: Introductionmentioning

confidence: 99%

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Textual Similarity Measurement Approaches: A Survey (1)

Abo-Elghit

Al-Zoghby

Hamza

2020

The Egyptian Journal of Language Engineering

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Survey research is appropriate and necessary to address certain research question types. This paper aims to provide a general overview of the textual similarity in the literature. Measuring textual similarity tends to have an increasingly important turn in related topics like text classification, recovery of specific information from data, clustering, topic retrieval, subject tracking, question answering, essay grading, summarization, and the nowadays trending Conversational Agents (CA), which is a program deals with humans through natural language conversation. Finding the similarity between terms is the essential portion of textual similarity, then used as a major phase for sentence-level, paragraph-level, and script-level similarities. In particular, we concern with textual similarity in Arabic. In the Arabic Language, applying Natural language Processing (NLP) tasks are very challenging indeed as it has many characteristics, which are considered as confrontations. However, many approaches for measuring textual similarity have been presented for Arabic text reviewed and compared in this paper.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Textual Similarity Measurement Approaches: A Survey (1)

Abo-Elghit

Al-Zoghby

Hamza

2020

The Egyptian Journal of Language Engineering

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“…Magooda et al [44] applied language models enriched with medical terms extracted from the Arabic Wikipedia. Finally, Malhas et al [45] exploited embeddings in different ways, including the computation of average word vectors and covariance matrices.…”

Section: Community Question Answering For Arabicmentioning

confidence: 99%

Language processing and learning models for community question answering in Arabic

Romeo

Martino

Belinkov³

et al. 2019

Information Processing & Management

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In this paper we focus on the problem of question ranking in community question answering (cQA) forums in Arabic. We address the task with machine learning algorithms using advanced Arabic text representations. The latter are obtained by applying tree kernels to constituency parse trees combined with textual similarities, including word embeddings. Our two main contributions are: (i) an Arabic language processing pipeline based on UIMA-from segmentation to constituency parsing-built on top of Farasa, a state-of-the-art Arabic language processing toolkit; and (ii) the application of long short-term memory neural networks to identify the best text fragments in questions to be used in our treekernel-based ranker. Our thorough experimentation on a recently released cQA dataset shows that the Arabic linguistic processing provided by Farasa produces strong results and that neural networks combined with tree kernels further boost the performance in terms of both efficiency and accuracy. Our approach also enables an implicit comparison between different processing pipelines as our tests on Farasa and Stanford parsers demonstrate.

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“…The RDI team developed a system (Magooda et al 2016), which used (i) external corpora (EC) crawled from to compute the probability of 〈 q s , a s 〉 to be relevant to q o , (ii) a LM trained on the annotated CQA-MD dataset; and (iii) the Arabic Wikipedia to boost the weights of medical terms.…”

Section: Semeval-2016 Shared Task On Arabic Cqamentioning

confidence: 99%

Arabic community question answering

Nakov¹,

Màrquez²,

Moschitti³

et al. 2018

Nat. Lang. Eng.

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We analyze resources and models for Arabic community Question Answering (cQA). In particular, we focus on CQA-MD, our cQA corpus for Arabic in the domain of medical forums. We describe the corpus and the main challenges it poses due to its mix of informal and formal language, and of different Arabic dialects, as well as due to its medical nature. We further present a shared task on cQA at SemEval, the International Workshop on Semantic Evaluation, based on this corpus. We discuss the features and the machine learning approaches used by the teams who participated in the task, with focus on the models that exploit syntactic information using convolutional tree kernels and neural word embeddings. We further analyze and extend the outcome of the SemEval challenge by training a meta-classifier combining the output of several systems. This allows us to compare different features and different learning algorithms in an indirect way. Finally, we analyze the most frequent errors common to all approaches, categorizing them into prototypical cases, and zooming into the way syntactic information in tree kernel approaches can help solve some of the most difficult cases. We believe that our analysis and the lessons learned from the process of corpus creation as well as from the shared task analysis will be helpful for future research on Arabic cQA.

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RDI_Team at SemEval-2016 Task 3: RDI Unsupervised Framework for Text Ranking

Cited by 6 publications

References 9 publications

Textual Similarity Measurement Approaches: A Survey (1)

Textual Similarity Measurement Approaches: A Survey (1)

Language processing and learning models for community question answering in Arabic

Arabic community question answering

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