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Attempting to identify and determine the sentiment polarity of one or more aspects (i.e., aspect words) in a sentence, Aspect-Level Sentiment Classification (abbreviated as ALSC) is a fine-grained sentiment classification task. Graph convolutional networks on dependency trees are now widely being used in related research to improve the accuracy of ALSC. The key to determining the polarity of aspectual emotions is to find the opinion, i.e., the opinion word, that is, most relevant to the aspectual emotion. However, in the dependency tree, a significant portion of aspect words and opinion words are not directly connected. And long-distance connections can lead to the model not paying enough attention to opinion words and losing information. In order to address this issue, by examining dependency syntactic structure and syntactic knowledge, we propose Aspect-opinion Sentence pattern Connection (ASC) to strengthen sentiment dependency graphs. We then develop the ASC-GCN to efficiently use the strengthened dependencies. Experimental results on four public benchmark datasets indicate that our approach achieves excellent performance on a lightweight model. [ABSTRACT FROM AUTHOR]

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Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis that presents great benefits to real-word applications. Recently, the methods utilizing graph neural networks over dependency trees are popular, but most of them merely considered if there exist dependencies between words, ignoring the types of these dependencies, which carry important information, as dependencies with different types have different effects. In addition, they neglected the correlations between dependency types and part-of-speech (POS) labels, which are helpful for utilizing dependency imformation. To address such limitations and the deficiency of insufficient syntactic and semantic feature mining, we propose a novel model containing three modules, which aims to leverage dependency trees more reasonably by distinguishing different dependencies and extracting beneficial syntactic and semantic features to further enhance model performance. To enrich word embeddings, we design a syntactic feature encoder (SynFE). In particular, we design Dependency-POS Weighted Graph Convolutional Network (DPGCN) to weight different dependencies by a graph attention mechanism we proposed. Additionally, to capture aspect-oriented semantic information, we design a semantic feature extractor (SemFE). Extensive experiments on five popular benchmark databases validate that our model can better employ dependency information and effectively extract favorable syntactic and semantic features to achieve new state-of-the-art performance. [ABSTRACT FROM AUTHOR]

• Discovering phrases in text by using community detection algorithms on syntax dependency trees. • Enhancing entity and sentence representations with a hierarchical attention mechanism. • The importance of key phrase recognition in relation extraction tasks is proved by different feature extraction methods. A method for extracting relations from sentences by utilizing their dependency trees to identify key phrases is presented in this paper. Dependency trees are commonly used in natural language processing to represent the grammatical structure of a sentence, and this approach builds upon this representation to extract meaningful relations between phrases. Identifying key phrases is crucial in relation extraction as they often indicate the entities and actions involved in a relation. The method uses community detection algorithms on the dependency tree to identify groups of related words that form key phrases, such as subject-verb-object structures. The experiments on the Semeval-2010 task8 dataset and the TACRED dataset demonstrate that the proposed method outperforms existing baseline methods. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis that presents great benefits to real-word applications. Recently, the methods utilizing graph neural networks over dependency trees are popular, but most of them merely considered if there exist dependencies between words, ignoring the types of these dependencies, which carry important information, as dependencies with different types have different effects. In addition, they neglected the correlations between dependency types and part-of-speech (POS) labels, which are helpful for utilizing dependency imformation. To address such limitations and the deficiency of insufficient syntactic and semantic feature mining, we propose a novel model containing three modules, which aims to leverage dependency trees more reasonably by distinguishing different dependencies and extracting beneficial syntactic and semantic features to further enhance model performance. To enrich word embeddings, we design a syntactic feature encoder (SynFE). In particular, we design Dependency-POS Weighted Graph Convolutional Network (DPGCN) to weight different dependencies by a graph attention mechanism we proposed. Additionally, to capture aspect-oriented semantic information, we design a semantic feature extractor (SemFE). Extensive experiments on five popular benchmark databases validate that our model can better employ dependency information and effectively extract favorable syntactic and semantic features to achieve new state-of-the-art performance.

Recent efforts to develop new machine translation evaluation methods have tried to account for allowable wording differences either in terms of syntactic structure or synonyms/paraphrases. This paper primarily considers syntactic structure, combining scores from partial syntactic dependency matches with standard local n-gram matches using a statistical parser, and taking advantage of N-best parse probabilities. The new scoring metric, expected dependency pair match (EDPM), is shown to outperform BLEU and TER in terms of correlation to human judgments and as a predictor of HTER. Further, we combine the syntactic features of EDPM with the alternative wording features of TERp, showing a benefit to accounting for syntactic structure on top of semantic equivalency features. [ABSTRACT FROM AUTHOR]

This article describes the generation of aggregated and elliptic sentences, using Dependency Trees connected by rhetorical relations as input. The system we have developed can generate both hypotactic and paratactic constructions with appropriate cue words, and various forms of ellipsis such as Gapping and Conjunction Reduction. We contend that Dependency Trees connected by rhetorical relations are excellent input for a generation system that has to generate ellipsis, and we propose a taxonomy of the most common Dutch cue words, grouped according to the kind of discourse relations they signal. Finally, we argue that syntactic aggregation should be performed in the Surface Realizer of a language generation system, because it requires access to language-specific syntactic information. [ABSTRACT FROM AUTHOR]