Topic
Dependency relation
About: Dependency relation is a research topic. Over the lifetime, 401 publications have been published within this topic receiving 4447 citations.
Papers published on a yearly basis
Papers
TL;DR: The authors proposed a concept-level sentiment analysis that merges linguistics, common-sense computing, and machine learning for improving the accuracy of tasks such as polarity detection, by allowing sentiments to flow from concept to concept based on the dependency relation of the input sentence, in particular, achieving a better understanding of the contextual role of each concept within the sentence and, hence, obtaining a polarity detector that outperforms state-of-the-art statistical methods.
Abstract: The Web is evolving through an era where the opinions of users are getting increasingly important and valuable. The distillation of knowledge from the huge amount of unstructured information on the Web can be a key factor for tasks such as social media marketing, branding, product positioning, and corporate reputation management. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions involves a deep understanding of natural language text by machines, from which we are still very far. To this end, concept-level sentiment analysis aims to go beyond a mere word-level analysis of text and provide novel approaches to opinion mining and sentiment analysis that enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data. A recent knowledge-based technology in this context is sentic computing, which relies on the ensemble application of common-sense computing and the psychology of emotions to infer the conceptual and affective information associated with natural language. Sentic computing, however, is limited by the richness of the knowledge base and by the fact that the bag-of-concepts model, despite more sophisticated than bag-of-words, misses out important discourse structure information that is key for properly detecting the polarity conveyed by natural language opinions. In this work, we introduce a novel paradigm to concept-level sentiment analysis that merges linguistics, common-sense computing, and machine learning for improving the accuracy of tasks such as polarity detection. By allowing sentiments to flow from concept to concept based on the dependency relation of the input sentence, in particular, we achieve a better understanding of the contextual role of each concept within the sentence and, hence, obtain a polarity detection engine that outperforms state-of-the-art statistical methods.
370 citations
TL;DR: In this article, the authors argue that features are immutable to the extent that they are central in a network of dependency relations, and they test a model of mutability based on asymmetric, unlabeled, pairwise dependency relations.
340 citations
4 Aug 2009
TL;DR: Using a transformation of dependency relation triples, features based on syntactic dependency relations can be utilized to improve performance on opinion mining by being converted into composite back-off features that generalize better than the regular lexicalized dependency relation features.
Abstract: We explore how features based on syntactic dependency relations can be utilized to improve performance on opinion mining. Using a transformation of dependency relation triples, we convert them into "composite back-off features" that generalize better than the regular lexicalized dependency relation features. Experiments comparing our approach with several other approaches that generalize dependency features or ngrams demonstrate the utility of composite back-off features.
182 citations
23 Aug 2004
TL;DR: A novel semantic role labeler based on dependency trees is developed by formulating the semantic role labeling as a classification problem of dependency relations into one of several semantic roles.
Abstract: In this paper, a novel semantic role labeler based on dependency trees is developed. This is accomplished by formulating the semantic role labeling as a classification problem of dependency relations into one of several semantic roles. A dependency tree is created from a constituency parse of an input sentence. The dependency tree is then linearized into a sequence of dependency relations. A number of features are extracted for each dependency relation using a predefined linguistic context. Finally, the features are input to a set of one-versus-all support vector machine (SVM) classifiers to determine the corresponding semantic role label. We report results on CoNLL2004 shared task data using the representation and scoring scheme adopted for that task.
120 citations
6 Apr 2014
TL;DR: A ConceptNet-based semantic parser that deconstructs natural language text into concepts based on the dependency relation between clauses is proposed that is domain-independent and is able to extract concepts from heterogeneous text.
Abstract: Concept-level text analysis is superior to word-level analysis as it preserves the semantics associated with multi-word expressions. It offers a better understanding of text and helps to significantly increase the accuracy of many text mining tasks. Concept extraction from text is a key step in concept-level text analysis. In this paper, we propose a ConceptNet-based semantic parser that deconstructs natural language text into concepts based on the dependency relation between clauses. Our approach is domain-independent and is able to extract concepts from heterogeneous text. Through this parsing technique, 92.21% accuracy was obtained on a dataset of 3,204 concepts. We also show experimental results on three different text analysis tasks, on which the proposed framework outperformed state-of-the-art parsing techniques.
74 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 10 |
| 2020 | 17 |
| 2019 | 18 |
| 2018 | 27 |
| 2017 | 27 |
| 2016 | 17 |