NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text
This paper describes the NeuroSent system that participated in SemEval 2018 Task 1. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a repository of user generated reviews. Thus, they are specific for sentiment analysis tasks. Then, tweets are converted in the corresponding vector representation and given as input to the neural network with the aim of learning the different semantics contained in each emotion taken into account by the SemEval task. The output layer has been adapted based on the characteristics of each subtask. Preliminary results obtained on the provided training set are encouraging for pursuing the investigation into this direction.
Code (0)
등록된 구현이 없습니다.
Tasks
Sentiment AnalysisWord EmbeddingsSimilar Papers 제목 키워드 기반
NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks Through a Multi-Domain Sentiment Model
This paper describes the NeuroSent system that participated in SemEval 2018 Task 3. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a…
Sentiment AnalysisWord EmbeddingsNEUROSENT-PDI at SemEval-2018 Task 7: Discovering Textual Relations With a Neural Network Model
Discovering semantic relations within textual documents is a timely topic worthy of investigation. Natural language processing strategies are generally used for linking chunks of text in order to extract information that…
Relation ExtractionWord EmbeddingsDeepPavlov at SemEval-2024 Task 8: Leveraging Transfer Learning for Detecting Boundaries of Machine-Generated Texts
The Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection shared task in the SemEval-2024 competition aims to tackle the problem of misusing collaborative human-AI writing. Although the…
Boundary DetectionText DetectionTransfer LearningDoris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features
In this paper we describe our participation to the Hyperpartisan News Detection shared task at SemEval 2019. Motivated by the late arrival of Doris Martin, we test a previously developed document classification system wh…
ClusteringDocument ClassificationGeneral ClassificationCMNEROne at SemEval-2022 Task 11: Code-Mixed Named Entity Recognition by leveraging multilingual data
Identifying named entities is, in general, a practical and challenging task in the field of Natural Language Processing. Named Entity Recognition on the code-mixed text is further challenging due to the linguistic comple…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER