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This paper describes our method for the task of Semantic Question Similarity in Arabic in the workshop on NLP Solutions for Under-Resourced Languages (NSURL). The aim is to build a model that is able to detect similar semantic questions in…

计算与语言 · 计算机科学 2020-04-28 Hana Al-Theiabat , Aisha Al-Sadi

Question semantic similarity (Q2Q) is a challenging task that is very useful in many NLP applications, such as detecting duplicate questions and question answering systems. In this paper, we present the results and findings of the shared…

计算与语言 · 计算机科学 2019-09-24 Haitham Seelawi , Ahmad Mustafa , Hesham Al-Bataineh , Wael Farhan , Hussein T. Al-Natsheh

Semantic textual relatedness is a broader concept of semantic similarity. It measures the extent to which two chunks of text convey similar meaning or topics, or share related concepts or contexts. This notion of relatedness can be applied…

计算与语言 · 计算机科学 2024-05-02 Sanad Malaysha , Mustafa Jarrar , Mohammed Khalilia

In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets". We design and ensemble two independent models, based on recurrent neural networks (Bi-LSTM), which operate at the…

This paper describes two systems that were used by the authors for addressing Arabic Sentiment Analysis as part of SemEval-2017, task 4. The authors participated in three Arabic related subtasks which are: Subtask A (Message Polarity…

计算与语言 · 计算机科学 2017-10-25 Samhaa R. El-Beltagy , Mona El Kalamawy , Abu Bakr Soliman

Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to…

计算与语言 · 计算机科学 2019-09-23 Hesham Al-Bataineh , Wael Farhan , Ahmad Mustafa , Haitham Seelawi , Hussein T. Al-Natsheh

NSURL-2019 Task 7 focuses on Named Entity Recognition (NER) in Farsi. This task was chosen to compare different approaches to find phrases that specify Named Entities in Farsi texts, and to establish a standard testbed for future researches…

计算与语言 · 计算机科学 2020-03-23 Nasrin Taghizadeh , Zeinab Borhanifard , Melika GolestaniPour , Heshaam Faili

This paper describes our submission to SemEval-2019 Task 7: RumourEval: Determining Rumor Veracity and Support for Rumors. We participated in both subtasks. The goal of subtask A is to classify the type of interaction between a rumorous…

计算与语言 · 计算机科学 2020-11-30 Ipek Baris , Lukas Schmelzeisen , Steffen Staab

This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task. Our system employs an attention-based recurrent neural network model that optimizes…

计算与语言 · 计算机科学 2017-03-17 Wenli Zhuang , Ernie Chang

This paper describes our system designed for SemEval-2022 Task 8: Multilingual News Article Similarity. We proposed a linguistics-inspired model trained with a few task-specific strategies. The main techniques of our system are: 1) data…

计算与语言 · 计算机科学 2022-04-12 Zihang Xu , Ziqing Yang , Yiming Cui , Zhigang Chen

We examine learning offensive content on Twitter with limited, imbalanced data. For the purpose, we investigate the utility of using various data enhancement methods with a host of classical ensemble classifiers. Among the 75 participating…

计算与语言 · 计算机科学 2019-06-11 Arun Rajendran , Chiyu Zhang , Muhammad Abdul-Mageed

In this paper, we present neural model architecture submitted to the SemEval-2019 Task 9 competition: "Suggestion Mining from Online Reviews and Forums". We participated in both subtasks for domain specific and also cross-domain suggestion…

计算与语言 · 计算机科学 2019-04-08 Samuel Pecar , Marian Simko , Maria Bielikova

The aim of SemEval-2024 Task 1, "Semantic Textual Relatedness for African and Asian Languages" is to develop models for identifying semantic textual relatedness (STR) between two sentences using multiple languages (14 African and Asian…

计算与语言 · 计算机科学 2024-04-15 Shubhashis Roy Dipta , Sai Vallurupalli

In this paper, we present the system submitted to "SemEval-2020 Task 12". The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach has been used to design our system. We…

计算与语言 · 计算机科学 2020-07-28 Hamada A. Nayel

In this paper, we present our submission to SemEval-2025 Task 8: Question Answering over Tabular Data. This task, evaluated on the DataBench dataset, assesses Large Language Models' (LLMs) ability to answer natural language questions over…

计算与语言 · 计算机科学 2025-08-04 Andreas Evangelatos , Giorgos Filandrianos , Maria Lymperaiou , Athanasios Voulodimos , Giorgos Stamou

This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages. The shared task aims at measuring the semantic textual relatedness between pairs of sentences, with a focus…

计算与语言 · 计算机科学 2024-06-10 Miaoran Zhang , Mingyang Wang , Jesujoba O. Alabi , Dietrich Klakow

Processing complex and ambiguous named entities is a challenging research problem, but it has not received sufficient attention from the natural language processing community. In this short paper, we present our participation in the English…

计算与语言 · 计算机科学 2022-03-08 Ngoc Minh Lai

We present the shared task on narrative similarity and narrative representation learning - NSNRL (pronounced "nass-na-rel"). The task operationalizes narrative similarity as a binary classification problem: determining which of two stories…

计算与语言 · 计算机科学 2026-04-24 Hans Ole Hatzel , Ekaterina Artemova , Haimo Paul Stiemer , Evelyn Gius , Chris Biemann

In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets". We participated in all subtasks for English tweets. We propose a Bi-LSTM architecture equipped with a multi-layer self…

Semantic Textual Relatedness holds significant relevance in Natural Language Processing, finding applications across various domains. Traditionally, approaches to STR have relied on knowledge-based and statistical methods. However, with the…

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