Aspect terms extraction and opinion terms extraction are two key problems of fine-grained Aspect Based Sentiment Analysis (ABSA). The aspect-opinion pairs can provide a global profile about a product or service for consumers and opinion mining systems. However, traditional methods can not directly output aspect-opinion pairs without given aspect terms or opinion terms. Although some recent co-extraction methods have been proposed to extract both terms jointly, they fail to extract them as pairs. To this end, this paper proposes an end-to-end method to solve the task of Pair-wise Aspect and Opinion Terms Extraction (PAOTE). Furthermore, this paper treats the problem from a perspective of joint term and relation extraction rather than under the sequence tagging formulation performed in most prior works. We propose a multi-task learning framework based on shared spans, where the terms are extracted under the supervision of span boundaries. Meanwhile, the pair-wise relations are jointly identified using the span representations. Extensive experiments show that our model consistently outperforms state-of-the-art methods.
We introduce a new task of modeling the role and function for on-line resource citations in scientific literature. By categorizing the on-line resources and analyzing the purpose of resource citations in scientific texts, it can greatly help resource search and recommendation systems to better understand and manage the scientific resources. For this novel task, we are the first to create an annotation scheme, which models the different granularity of information from a hierarchical perspective. And we construct a dataset SciRes, which includes 3,088 manually annotated resource contexts. In this paper, we propose a possible solution by using a multi-task framework to build the scientific resource classifier (SciResCLF) for jointly recognizing the role and function types. Then we use the classification results to help a scientific resource recommendation (SciResREC) task. Experiments show that our model achieves the best results on both the classification task and the recommendation task. The SciRes dataset is released for future research.
Short texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models. To deal with the insufficiency, we propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. Our model can generate more interpretable topics as well as document clusters. We develop an effective Gibbs sampling algorithm favoured by the fully local conjugacy in the model. Extensive experiments demonstrate that our model achieves better performance in terms of document clustering and topic coherence.