ATE_ABSITA@EVALITA2020 Task

***************************** 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:<br> website: http://www.di.uniba.it/~swap/ate_absita/index.html<br> email: [e-mail]<br> email: [e-mail] PLEASE CITE: @InProceedings{ateabsita2020,<br> author = {Lorenzo de Mattei and Graziella de Martino and Andrea Iovine and Alessio Miaschi and Marco Polignano and Giulia Rambelli},<br> title = {{ATE\_ABSITA@EVALITA2020: Overview of the Aspect Term Extraction and Aspect-based Sentiment Analysis Task}},<br> booktitle = {{Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020)}},<br> editor = {Basile, Valerio and Croce, Danilo and Di Maro, Maria and Passaro, Lucia C.},<br> year = {2020},<br> publisher = {CEUR.org},<br> address = {Online}<br> }***************************** 任务描述 ********************************* 在本次挑战赛中，我们将推出三项针对不同任务的标注任务，分别为**方面项提取（Aspect Term Extraction, ATE）**、**基于方面的情感分析（Aspect Based Sentiment Analysis, ABSA）**以及**句子级情感分析（Sentiment Analysis, SA）**。 方面项提取（ATE）指在无需预先给定候选方面词列表的前提下，从文本中识别出“方面”的任务。根据学术文献中的标准定义，若某一词项或短语与表达针对其情感极性的“评价词”共同出现，则该词项/短语可被认定为方面词。更多细节与示例可参阅：http://www.di.uniba.it/~swap/ate_absita/examples.html 基于方面的情感分析（ABSA）是情感分析的进阶分支，旨在捕捉自然语言文本中面向特定方面的观点表达。在该任务中，需识别每个被提及方面的情感极性。 情感分析（又称观点挖掘）是识别用户对特定文本内容所持观点的任务，其常见实现形式为标注任务，即通过为文本片段标注积极、消极或中性标签来完成标注。在本次句子级情感分析（SA）任务中，我们将采用用户为参评商品留下的评分作为情感极性的取值，该评分被定义为1至5之间的整数。 *************************** ATE ABSITA - EVALITA 2020 ************************** 联系方式:<br> 官方网站: http://www.di.uniba.it/~swap/ate_absita/index.html<br> 官方邮箱: [e-mail]<br> 联系邮箱: [e-mail] 请引用如下文献：@InProceedings{ateabsita2020,<br> 作者 = {Lorenzo de Mattei and Graziella de Martino and Andrea Iovine and Alessio Miaschi and Marco Polignano and Giulia Rambelli},<br> 论文标题 = {{ATE_ABSITA@EVALITA2020: 方面项提取与基于方面情感分析任务综述}},<br> 会议论文集名称 = {{第七届意大利语自然语言处理与语音工具评测大会论文集（EVALITA 2020）}},<br> 编者 = {Basile, Valerio and Croce, Danilo and Di Maro, Maria and Passaro, Lucia C.},<br> 出版年份 = {2020},<br> 出版机构 = {CEUR.org},<br> 出版地点 = {线上}<br> }