ATE_ABSITA@EVALITA2020 Task
| Main Author: | Marco Polignano |
|---|---|
| Format: | info dataset Journal |
| Bahasa: | ita |
| Terbitan: |
, 2020
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/4340411 |
Daftar Isi:
- ***************************** TASK DESCRIPTION ********************************* In our challenge, we would like to propose three different annotation tasks regarding Aspect Term Extraction (ATE), Aspect Based Sentiment Analysis (ABSA), and sentence Sentiment Analysis (SA). Aspect Term Extraction (ATE) is the task of identifying an "aspect" in a text without knowing a priori the list that contains it. According to the literature definition, a term/phrase is considered as an aspect when it co-occurs with “opinion words” that indicate a sentiment polarity on it. More details and examples are available at: http://www.di.uniba.it/~swap/ate_absita/examples.html Aspect-based Sentiment Analysis (ABSA) is an evolution of Sentiment Analysis that aims at capturing the aspect-level opinions expressed in natural language texts. In the Aspect-based Sentiment Analysis (ABSA) task, the polarity of each expressed aspect is recognized. Sentiment Analysis (or Opinion Mining) is the task of identifying what the user thinks about a particular piece of text. In particular, it often takes the form of an annotation task with the purpose of annotating a portion of text with a positive, negative, or neutral label. In our Sentiment Analysis (SA) task, the polarity of the review is provided. In particular, we decided to use the score left by the user at the item as value of polarity. It is defined as an integer number into the range 1:5. *************************** ATE ABSITA - EVALITA 2020 ************************** Contacts: website: http://www.di.uniba.it/~swap/ate_absita/index.html email: ate.absita.evalita2020@gmail.com email: marco.polignano@uniba.it PLEASE CITE: @InProceedings{ateabsita2020, author = {Lorenzo de Mattei and Graziella de Martino and Andrea Iovine and Alessio Miaschi and Marco Polignano and Giulia Rambelli}, title = {{ATE\_ABSITA@EVALITA2020: Overview of the Aspect Term Extraction and Aspect-based Sentiment Analysis Task}}, booktitle = {{Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020)}}, editor = {Basile, Valerio and Croce, Danilo and Di Maro, Maria and Passaro, Lucia C.}, year = {2020}, publisher = {CEUR.org}, address = {Online} }