annotations/Ed/paper_49.txt.json

[
  {
    "file": "paper_49.txt",
    "start": 1006,
    "end": 1043,
    "label": "Format",
    "user": "Ed",
    "text": "Wang et al., 2016aDai and Song,  2019"
  },
  {
    "file": "paper_49.txt",
    "start": 177,
    "end": 1179,
    "label": "Lacks synthesis",
    "user": "Ed",
    "text": " In contrast, Aspect-based Sentiment Analysis (ABSA) is an aspect or entity oriented fine-grained sentiment analysis task. The most three basic subtasks are Aspect Term Extraction (ATE) (Hu and Liu, 2004;Yin et al., 2016;Li et al., 2018b;Xu et al., 2018;Ma et al., 2019;Chen and Qian, 2020;, Aspect Sentiment Classification (ASC) (Wang et al., 2016b;Tang et al., 2016;Ma et al., 2017;Fan et al., 2018;Li et al., 2018a;Li et al., 2021) and Opinion Term Extraction (OTE) Cardie, 2012, 2013;Fan et al., 2019;Wu et al., 2020b). The studies solve these tasks separately and ignore the dependency between these subtasks. Therefore, some efforts devoted to couple the two subtasks and proposed effective models to jointly extract aspect-based pairs. This kind of work mainly has two tasks: Aspect and Opinion Term Co-Extraction (AOTE) (Wang et al., 2016aDai and Song,  2019; Wang and Pan, 2019;Wu et al., 2020a) and Aspect-Sentiment Pair Extraction (ASPE) (Ma et al., 2018;Li et al., 2019a,b;He et al., 2019)."
  },
  {
    "file": "paper_49.txt",
    "start": 1535,
    "end": 1540,
    "label": "Unsupported claim",
    "user": "Ed",
    "text": "BERT "
  },
  {
    "file": "paper_49.txt",
    "start": 1739,
    "end": 1756,
    "label": "Format",
    "user": "Ed",
    "text": " Wu et al., 2020a"
  },
  {
    "file": "paper_49.txt",
    "start": 1865,
    "end": 1944,
    "label": "Unsupported claim",
    "user": "Ed",
    "text": "limitations related to existing works by enriching the expressiveness of labels"
  },
  {
    "file": "paper_49.txt",
    "start": 1181,
    "end": 2322,
    "label": "Lacks synthesis",
    "user": "Ed",
    "text": "Most recently, Peng et al. (2020) first proposed the ASTE task and developed a two-stage pipeline framework to couple together aspect extraction, aspect sentiment classification and opinion extraction. To further explore this task, (Mao et al., 2021;Chen et al., 2021a) transformed ASTE to a machine reading comprehension problem and utilized the shared BERT encoder to obatin the triplets after multiple stages decoding. Another line of research focuses on designing a new tagging scheme that makes the model can extract the triplets in an endto-end fashion Wu et al., 2020a;Xu et al., 2021;Yan et al., 2021). For instance,  proposed a positionaware tagging scheme, which solves the limitations related to existing works by enriching the expressiveness of labels. Wu et al. (2020a) proposed a grid tagging scheme, similar to table filling (Miwa and Sasaki, 2014;Gupta et al., 2016), to solve this task in an end-to-end manner. Yan et al. (2021) converted ASTE task into a generative formulation. However, these approaches generally ignore the relations between words and linguistic features which effectively promote the triplet extraction."
  }
]