id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_m77zzbb2kfbtxcgjfd5pecfqra	Lizhen Qu	Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM	2014	14	.pdf	application/pdf	8454	780	60	Extracting instances of sentiment-oriented relations from user-generated web documents is sentiment-bearing expressions, and it can simultaneously recognize instances of both binary (polarity) and ternary (comparative) relations with regard to entity mentions of interest. Therefore, in this paper, we identify instances of both sentiment polarities and comparative relations for entities of interest simultaneously. Wiebe et al., 2005; Hu and Liu, 2004) in the following ways: i) both sentiment polarities and comparative relations are annotated; ii) all mentioned entities are disambiguated; and iii) no subjective expressions are annotated, unless they are part of entity expressions for training and can predict both sentiment polarities and comparative relations. mention-based relation instances expressed in a sentence. possible combinations of mention-based relation instances and their textual evidences (cf. edge correspond to an instance of mention-based relation and the associated textual evidence. sSoR and label the corresponding tuples with the relation types of the edges from an MRG.	./cache/work_m77zzbb2kfbtxcgjfd5pecfqra.pdf	./txt/work_m77zzbb2kfbtxcgjfd5pecfqra.txt