You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Past work in computational sarcasm deals primarily with sarcasm detection. In this paper, we introduce a novel, related problem: sarcasm target identification (, extracting the target of ridicule in a sarcastic sentence). We present an introductory approach for sarcasm target identification. Our approach employs two types of extractors: one based on rules, and another consisting of a statistical classifier. To compare our approach, we use two baselines: a na "ive baseline and another baseline based on work in sentiment target identification. We perform our experiments on book snippets and tweets, and show that our hybrid approach performs better than the two baselines and also, in comparison with using the two extractors individually. Our introductory approach establishes the viability of sarcasm target identification, and will serve as a baseline for future work.

Cited Abstract(s):
[1]: Online reviews are often accompanied with numerical ratings provided by users for a set of service or product aspects. We propose a statistical model which is able to discover corresponding topics in text and extract textual evidence from reviews supporting each of these aspect ratings ‐ a fundamental problem in aspect-based sentiment summarization (Hu and Liu, 2004a). Our model achieves high accuracy, without any explicitly labeled data except the user provided opinion ratings. The proposed approach is general and can be used for segmentation in other applications where sequential data is accompanied with correlated signals.
Related to sarcasm target identification is sentiment target identification. Sentiment target identification deals with identifying the entity towards which sentiment is expressed in a sentence. present an approach to extract opinion words and targets collectively from a dataset. Aspect identification for sentiment has also been studied. This deals with extracting aspects of an entity (for example, color, weight, battery in case of a cell phone). Probabilistic topic models have been commonly used for the same. present a probabilistic topic model that jointly estimates sentiment and aspect in order to achieve sentiment summarization. perform multi-aspect sentiment analysis using a topic model. Several other topic model-based approaches to aspect extraction have been reported [1] . To the best of our knowledge, ours is the first work that deals with sarcasm target identification.