LightRel at SemEval-2018 Task 7: Lightweight and Fast Relation Classification
We present LightRel, a lightweight and fast relation classifier. Our goal is to develop a high baseline for different relation extraction tasks. By defining only very few data-internal, word-level features and external knowledge sources in the form of word clusters and word embeddings, we train a fast and simple linear classifier
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Feature EngineeringGeneral ClassificationRelationRelation ClassificationRelation ExtractionWord EmbeddingsSimilar Papers 제목 키워드 기반
LightRel SemEval-2018 Task 7: Lightweight and Fast Relation Classification
We present LightRel, a lightweight and fast relation classifier. Our goal is to develop a high baseline for different relation extraction tasks. By defining only very few data-internal, word-level features and external k…
ClassificationGeneral ClassificationRelationRelation Classification+2Team TMA at SemEval-2022 Task 8: Lightweight and Language-Agnostic News Similarity Classifier
We present our contribution to the SemEval 22 Share Task 8: Multilingual news article similarity. The approach is lightweight and language-agnostic, it is based on the computation of several lexicographic and embedding-b…
ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight Models
A robust comprehension of sarcasm detection iscritical for creating artificial systems that can ef-fectively perform sentiment analysis in writtentext. In this work, we investigate AI approachesto identifying whether a t…
Sarcasm DetectionSentiment AnalysisUTFPR at SemEval 2020 Task 12: Identifying Offensive Tweets with Lightweight Ensembles
Offensive language is a common issue on social media platforms nowadays. In an effort to address this issue, the SemEval 2020 event held the OffensEval 2020 shared task where the participants were challenged to develop s…
FMI_SU_Yotkova_Kastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals
SemEval-2026 Task 13 investigates machine-generated code detection across multiple programming languages and application scenarios, asking participating systems to generalize to unseen languages and domains. This paper d…
Binary Classification