Y. Shen

Y. Shen, R. Wang, W. Zuo et al.

Visual place recognition (VPR) is a key issue for robotics and autonomous systems. For the trade-off between time and performance, most of methods use the coarse-to-fine hierarchical architecture, which consists of retrieving top-N candidates using global features, and re-ranking top-N with local features. However, since the two types of features are usually processed independently, re-ranking may harm global retrieval, termed re-ranking confusion. Moreover, re-ranking is limited by global retrieval. In this paper, we propose a tightly coupled learning (TCL) strategy to train triplet models. Different from original triplet learning (OTL) strategy, it combines global and local descriptors for joint optimization. In addition, a bidirectional search dynamic time warping (BS-DTW) algorithm is also proposed to mine locally spatial information tailored to VPR in re-ranking. The experimental results on public benchmarks show that the models using TCL outperform the models using OTL, and TCL can be used as a general strategy to improve performance for weakly supervised ranking tasks. Further, our lightweight unified model is better than several state-of-the-art methods and has over an order of magnitude of computational efficiency to meet the real-time requirements of robots.

K. Lei, S. Si, D. Wen et al.

Medical synonym identification has been an important part of medical natural language processing (NLP). However, in the field of Chinese medical synonym identification, there are problems like low precision and low recall rate. To solve the problem, in this paper, we propose a method for identifying Chinese medical synonyms. We first selected 13 features including Chinese and English features. Then we studied the synonym identification results of each feature alone and different combinations of the features. Through the comparison among identification results, we present an optimal combination of features for Chinese medical synonym identification. Experiments show that our selected features have achieved 97.37% precision rate, 96.00% recall rate and 97.33% F1 score.

H. Palangi, L. Deng, Y. Shen et al.

In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike common IR methods that use bag of words representation for queries and documents, we treat them as a sequence of words and use long short term memory (LSTM) to capture contextual dependencies. To the best of our knowledge, this is the first time that LSTM is applied to information retrieval tasks. Unlike training traditional LSTMs, the training strategy is different due to the special nature of information retrieval problem. Experimental evaluation on an IR task derived from the Bing web search demonstrates the ability of the proposed method in addressing both lexical mismatch and long-term context modelling issues, thereby, significantly outperforming existing state of the art methods for web document retrieval task.