We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by considering a portmanteau of contents and concepts. It then employs a stack-wise graph-cutting algorithm to extract communities of related authors. Experimental results show that our multi-aspect vector space model can gain higher performance than other knowledge-centered competitors in linking short-text authors.

We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by considering a portmanteau of contents and concepts. It then employs a stack-wise graph-cutting algorithm to extract communities of related authors. Experimental results show that our multi-aspect vector space model can gain higher performance than other knowledge-centered competitors in linking short-text authors.

Addressing the challenge of matching queries with the right experts amid temporal-textual inconsistencies, we present a novel approach that combines an attention-based text embedding model with a continuous-time module. This method effectively maps queries to relevant experts by analyzing concept-oriented vectors and user behavior, demonstrating significant effectiveness on StackOverflow and Yahoo datasets.

Finding the most suitable individual to answer a question using brief content has important usages, including the community of question answering systems and online recommender frameworks. However, one must tackle challenges: Disregarding the indispensable noise in short text contents, authors usually answer the input query with mismatched words that can negatively influence the textual relevance. Moreover, many vocabularies imply various alterations. Finally, not every expert is eager to answer an input query given the time constraint, named the reluctance dilemma. To overcome the challenges, we devise a novel embedding approach that constructs context-aware vectors. We then extract the knowledge domains out of the online contextual content. While we track user textual-temporal behavioral patterns via an infinite continuous-time module, we recommend a set of experts pertinent to the given query and willingly provide the response during the expected time. Experimental results on two real-world datasets ofStackOverflowandYahooshow that our online time-sensitive value-wise transformer can achieve higher effectiveness and efficiency versus other trending rivals in online expert recommendation systems. In addition, we empirically experience that Fourier transformers can automatically infer multi-aspect base signals and overpass manual discrete-time models in obtaining time-specific user profiles.

Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. First, the input short-text contents are noisy, ambiguous, and do not follow the grammatical rules. Second, traditional text mining methods fail to effectively extract concepts through words and phrases. Third, the textual contents are temporally skewed, which can affect the semantic understanding by multiple time facets. Finally, using knowledge-bases can make the results biased to the content of the external database and deviate the meaning from the input short text corpus. To overcome these challenges, we devise a neural network-based temporal-textual framework that generates the subgraphs with highly correlated authors from short-text contents. Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors. Experimental results show that compared to other knowledge-centered competitors, our multi-aspect vector space model can achieve a higher performance in linking short-text authors. In addition, given the author linking task, the more comprehensive the dataset is, the higher the significance of the extracted concepts will be.

One versus all (OVA) decision trees learn k individual binary classifiers, each one to distinguish the instances of a single class from the instances of all other classes. Thus OVA is different from existing data stream classification schemes whose majority use multiclass classifiers, each one to discriminate among all the classes. This paper advocates some outstanding advantages of OVA for data stream classification. First, there is low error correlation and hence high diversity among OVA's component classifiers, which leads to high classification accuracy. Second, OVA is adept at accommodating new class labels that often appear in data streams. However, there also remain many challenges to deploy traditional OVA for classifying data streams. First, as every instance is fed to all component classifiers, OVA is known as an inefficient model. Second, OVA's classification accuracy is adversely affected by the imbalanced class distribution in data streams. This paper addresses those key challenges and consequently proposes a new OVA scheme that is adapted for data stream classification. Theoretical analysis and empirical evidence reveal that the adapted OVA can offer faster training, faster updating and higher classification accuracy than many existing popular data stream classification algorithms.