Titlebook: Information Retrieval; 27th China Conferenc Hongfei Lin,Min Zhang,Liang Pang Conference proceedings 2021 Springer Nature Switzerland AG 202

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Interaction-Based Document Matching for Implicit Search Result Diversificationost of previous approaches of search result diversification use pre-trained embeddings to represent the candidate documents. These representation-based approaches lose fine-grained matching signals. In this paper, we propose a new supervised framework leveraging interaction-based neural matching sig

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Modelling Dynamic Item Complementarity with Graph Neural Network for Recommendation. However, there are two key limitations: 1) Most previous methods use co-occurrence to quantify item complementary relationship, which lacks theoretical support and overlooks the fact that co-occurrence is only a necessary but not sufficient condition to identify item complementarity. 2) Most studi

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Iterative Strict Density-Based Clustering for News Stream news reports vary greatly in different countries, languages and news-topics, clustering diverse news has proven to be a big challenge for all researchers. The results of current clustering methods expose their inability to detect fine-grained topics. They tend to detect topics on a coarse-grained s

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A Pre-LN Transformer Network Model with Lexical Features for Fine-Grained Sentiment Classificationmost existing models can effectively identify the extreme polarity (extremely positive, extremely negative), we find they cannot distinguish the intermediate polarity (generally positive, neutral, generally negative) clearly. Besides, the models based on convolutional neural networks (CNNs) and recu

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Adversarial Context-Aware Representation Learning of Multiword Expressionspressions (e.g., go banana) can not be inferred by word composition. Most current methods regard a multiword expression as a single word, and learn its representation in the same way as word representations. However, many multiword expressions are ambiguous, that they express distinct meanings (lite