Efficient Representation of Adhoc Constraints

By Guang Qiu, B. Liu, Jiajun Bu, Chun Chen. International Joint Conference on Artificial Intelligence, 2009

392 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ee1f82a46527c31dff6112a754e202d5e69baaf6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Duyu Tang
2. Bing Liu
3. B. Liu
4. Lei Zhang
5. Furu Wei
6. Bing Qin
7. Minlie Huang
8. Ting-Wei Liu

Citations in the corpus

• Sentiment Analysis and Opinion Mining (2012, unknown): “In Qiu et al. (2009) and Qiu et al.”
• Survey on Aspect-Level Sentiment Analysis (2016, background): “In [33] (an extended version was published later in [53]), and its extension [34], aspect detection and sentiment lexicon expansion are seen as interrelated problems for which a dou-”
• Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach (2014, background): “Qiu et al. (2009; 2011) adopt dependency relations between sentiment words and aspect words.”
• A Survey of Opinion Mining and Sentiment Analysis (2012, methodology): “In [84, 85], a double-propagation method was proposed to extraction both opinion words and aspects together.”
• Target-Dependent Sentiment Classification With BERT (2019, background): “Multiple sentiment lexicons are built for this purpose [22], [30], [37], taking a large amount of human labor, but they are difficult to be transferred into another domain.”
• Cross-Domain Co-Extraction of Sentiment and Topic Lexicons (2012, methodology): “Recently, Qiu et al. (2009) proposed a rule-based semi-supervised learning methods for lexicon extraction.”
• Extracting and Ranking Product Features in Opinion Documents (2010, methodology): “It has been shown in (Qiu et al., 2009 ) that many feature and opinion word pairs have long range dependencies.”
• A review of natural language processing techniques for opinion mining systems (2017, methodology): “[85] presented a double propagation algorithm to discover pairs of opinion word and opinion target.”
• Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks (2018, background): “Multiple sentiment lexicons are built for this purpose [14,18,22].”
• Learning to Identify Review Spam (2011, methodology): “The researchers from AI community have developed various sentiment analysis tasks, including sentiment classification [Li et al., 2010; Wu and Huberman, 2010], opinion retrieval [Furuse et al., 2007], opinion extraction [Qiu et al., 2009], to serve users' need.”
• Aspect-Oriented Opinion Mining from User Reviews in Croatian (2013, methodology): “We use the inflectional lexicon from Šnajder et al. (2008) for lemmatization, POS tagger from Agić et al.”
• Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora (2016, background): “Most previous work seeks to enrich or enlarge existing lexicons (Qiu et al., 2009; San Vicente et al., 2014; Velikovich et al., 2010), emphasizing recall over precision.”
• Aspect-based sentiment analysis of movie reviews on discussion boards (2010, background): “Qiu et al. [14] proposed a propagation approach for extracting a large number of sentiment words and assigning polarity.”