Lucia C. Passaro
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    • Adapting Language Models with Continual Learning for Temporal Drifts
    • An Experimental Comparison of the Most Popular Approaches to Fake News Detection
    • Challenging the Abilities of Large Language Models in Italian: A Community Initiative
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    • Challenging Specialized Transformers on Zero-Shot Classification
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    • Bias Discovery within Human Raters: A Case Study of the Jigsaw Dataset
    • Evaluating Pre-Trained Transformers on Italian Administrative Texts
    • GQA-it: Italian Question Answering on Image Scene Graphs
    • GQA-it: Italian Question Answering on Image Scene Graphs
    • In-Context Annotation of Topic-Oriented Datasets of Fake News: A Case Study on the Notre-Dame Fire Event
    • MATE, a Meta Layer between Natural Language and Database
    • Preface to the Sixth Workshop on Natural Language for Artificial Intelligence (NL4AI)
    • Leveraging CLIP for Image Emotion Recognition
    • Preface to the Fifth Workshop on Natural Language for Artificial Intelligence (NL4AI)
    • CAPISCO@CONcreTEXT 2020: (Un)Supervised Systems to Contextualize Concreteness with Norming Data
    • EVALITA 2020: Overview of the 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian
    • EVALITA 2020: Overview of the 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian
    • FRAQUE: A Frame-Based Question-Answering System for the Public Administration Domain
    • Less Is MORE: A MultimOdal System for Tag Refinement
    • Lessons Learned from EVALITA 2020 and Thirteen Years of Evaluation of Italian Language Technology
    • Preface to the EVALITA 2020 Proceedings
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    • Voices of the Great War: A Richly Annotated Corpus of Italian Texts on the First World War
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    • INFORMed PA: A NER for the Italian Public Administration Domain
    • Learning Affect with Distributional Semantic Models
    • Evaluating Context Selection Strategies to Build Emotive Vector Space Models
    • Exploiting Emotive Features for the Sentiment Polarity Classification of Tweets
    • Extracting Terms with Extra
    • FB-NEWS15: A Topic-Annotated Facebook Corpus for Emotion Detection and Sentiment Analysis
    • Pos-Patterns or Syntax? Comparing Methods for Extracting Word Combinations
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    • ItEM: A Vector Space Model to Bootstrap an Italian Emotive Lexicon
    • ``Il Piave Mormorava...'': Recognizing Locations and Other Named Entities in Italian Texts on the Great War
    • Computational Analysis of Historical Documents: An Application to Italian War Bulletins in World War I and II
    • The CoLing Lab System for Sentiment Polarity Classification of Tweets

Evaluating Context Selection Strategies to Build Emotive Vector Space Models

Jan 1, 2016·
Passaro, Lucia
,
Lenci, Alessandro
Type
Conference-Paper
Publication
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)
publications
Last updated on Jan 1, 2016
← Learning Affect with Distributional Semantic Models Jan 1, 2017
Exploiting Emotive Features for the Sentiment Polarity Classification of Tweets Jan 1, 2016 →

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