We participated in the Deep Learning Track at TREC 2019. We built a ranking system which combines a search component based on BM25 and a semantic matching component using pretraining knowledge. Our best run achieves N...
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Social medial become our public ways to share our information in our lives. Crisis management via social medial is becoming indispensable for its tremendous information. While deep learning shows surprising outcome in...
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We participate in the Complex Answer Retrieval(CAR) track at TREC 2019. We applied several useful models in this work. In the rough ranking, we applied doc2query model to predict queries and retrieve using BM25. In th...
This paper describes our work in the background linking task and entity ranking task in TREC 2018 News Track. We explore four methods in background linking task and two methods in entity ranking task. All of our metho...
Recently, large pre-trained models (LPM) have achieved great success, which provides rich feature representation for downstream tasks. Pre-training and then fine-tuning is an effective way to utilize LPM. However, the...
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In this paper we report on our participation in the Trec 2019 Decision Track[1] which aims to provide a venue for research on retrieval methods that promote better decision making with search engines and develop new o...
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Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant i...
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Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i.e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position. Based on this idea, we propose a novel deep architecture, namely Match-SRNN, to model the recursive matching structure. Firstly, a tensor is constructed to capture the word level interactions. Then a spatial RNN is applied to integrate the local interactions recursively, with importance determined by four types of gates. Finally, the matching score is calculated based on the global interaction. We show that, after degenerated to the exact matching scenario, Match-SRNN can approximate the dynamic programming process of longest common subsequence. Thus, there exists a clear interpretation for Match-SRNN. Our experiments on two semantic matching tasks showed the effectiveness of Match-SRNN, and its ability of visualizing the learned matching structure.
Predicting anchor links across social networks has important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation. One challenging problem is: whether...
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Predicting anchor links across social networks has important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation. One challenging problem is: whether and to what extent we can address the anchor link prediction problem, if only structural information of networks is available. Most existing methods, unsupervised or supervised, directly work on networks themselves rather than on their intrinsic structural regularities, and thus their effectiveness is sensitive to the high dimension and sparsity of networks. To offer a robust method, we propose a novel supervised model, called PALE, which employs network embedding with awareness of observed anchor links as supervised information to capture the major and specific structural regularities and further learns a stable cross-network mapping for predicting anchor links. Through extensive experiments on two realistic datasets, we demonstrate that PALE significantly outperforms the state-of-the-art methods.
Retail transaction data conveys rich preference information on brands and goods from customers. How to mine the transaction data to provide personalized recommendation to customers becomes a critical task for retailer...
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ISBN:
(纸本)9781450325981
Retail transaction data conveys rich preference information on brands and goods from customers. How to mine the transaction data to provide personalized recommendation to customers becomes a critical task for retailers. Previous recommendation methods either focus on the user-product matrix and ignore the transactions, or only use the partial information of transactions, leading to inferior performance in recommendation. Inspired by association rule mining, we introduce association pattern as a basic unit to capture the correlation between products from both intra- and intertransactions. A Probabilistic model over the Association Patterns (PAP for short) is then employed to learn the potential shopping interests and also to provide personalized recommendations. Experimental results on two real world retail data sets show that our proposed method can outperform the state-of-the-art recommendation methods. Copyright 2014 ACM.
The present study explores the application of Landsat-9 OLI and Sentinel-1 SAR data for effective lineament extraction and structural mapping in the Proterozoic North Singhbhum Mobile Belt, Eastern India, an area char...
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