A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval

By Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, Grégoire Mesnil. 2014

727 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation (2017, methodology): “HCTI (Shao, 2017) Third place overall is obtained by HCTI with a model similar to a convolutional Deep Structured Semantic Model (CDSSM) (Chen et al., 2015; Huang et al., 2013).”
Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval (2015, methodology): “…multi-task framework is extensible to other meth-ods for sentence/document representations, such as those based on convolutional networks (Kalchbren-ner et al., 2014; Shen et al., 2014; Gao et al., 2014b), parse tree structure (Irsoy and Cardie, 2014), and run-time inference (Le and Mikolov, 2014).”
End-to-End Neural Ad-hoc Ranking with Kernel Pooling (2017, methodology): “Neural ranking baselines include DRMM [9], CDSSM [21], and a simple embedding-based translation model, Trans.”
Character-level Convolutional Networks for Text Classification (2015, methodology): “In particular, these ConvNet approaches use words as a basis, in which character-level features extracted at word [28] or word n-gram [29] level form a distributed representation.”
Stacked Attention Networks for Image Question Answering (2015, methodology): “As [25, 22, 6] show that LSTMs and CNNs are powerful to capture the semantic meaning of texts, we explore both models for question representations in this study.”
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses (2015, background): “Specifically, the forward pass proceeds with the following recurrence, for t = 1, . . . , T : ht = σ(s > t Win + h > t−1Whh), ot = h > t Wout (2) where σ is a non-linear function applied elementwise, in our case the logistic sigmoid.”
Neural Approaches to Conversational AI (2018, neutral): “ty of a pair of inputs (x;y). They can be applied to a wide range of tasks depending on the deﬁnition of (x;y). For example, (x;y) is a query-document pair for Web search ranking (Huang et al., 2013; Shen et al., 2014), a document pair in recommendation (Gao et al., 2014b), a question-answer pair in”
Character-Aware Neural Language Models (2015, background): “ecially well-suited to work with CNNs, adaptively combining local features detected by the individual ﬁlters. CNNs have already proven to be been successful for many NLP tasks (Collobert et al. 2011; Shen et al. 2014; Kalchbrenner, Grefenstette, and Blunsom 2014; Kim 2014; Zhang, Zhao, and LeCun 201”
From captions to visual concepts and back (2014, background): “The DSSM was initially proposed to model the semantic relevance between textual search queries and documents, and is extended in this work to replace the query vector in the original DSSM by the image vector computed from the deep convolutional network.”
Hierarchical Attention Networks for Document Classification (2016, background): “Attention serves two beneﬁts: not only does it often result in better performance, but it also provides insight into which words and sentences contribute to the classiﬁcation decision which can be of value in applications and analysis (Shen et al., 2014; Gao et al., 2014).”
The Design and Implementation of XiaoIce, an Empathetic Social Chatbot (2018, background): “4 DSSM stands for Deep Structured Semantic Models (Huang et al. 2013; Shen et al. 2014), or more generally, Deep Semantic Similarity Model (Gao et al.”
Recent Trends in Deep Learning Based Natural Language Processing (2017, background): “6) was explored in [55] for information retrieval.”
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks (2015, background): “The supervised approaches [5, 8, 23, 6] are usually based on deep neural network architectures, such as recursive neural tensor networks (RNTNs) [24] or convolutional neural networks (CNNs) [11].”
A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion (2015, methodology): “The authors apply the discriminative pairwise neural model described in [34] to measure similarity between queries.”
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks (2014, methodology): “…on token-level applications (e.g., POS tagging) by Collobert et al. (2011), CNN has been used in systems for entity search, sentence modeling, word embedding learning, product feature mining, and so on (Xu and Sarikaya, 2013; Gao et al., 2014; Shen et al., 2014; Kalchbrenner et a r X i v : 1412 .”
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base (2015, methodology): “In this work, we adapt a convolutional neural network (CNN) framework (Shen et al., 2014b; Shen et al., 2014a; Gao et al., 2014) to this matching problem.”
Embedding Entities and Relations for Learning and Inference in Knowledge Bases (2014, methodology): “This general framework for relationship modeling also applies to the recent deep-structured semantic model (Huang et al., 2013; Shen et al., 2014a;b; Gao et al., 2014; Yih et al., 2014), which learns the relevance or a single relation between a pair of word sequences.”
Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval (2015, methodology): “, 2012) and in semantic modeling (Huang et al., 2013; Shen et al., 2014; Gao et al., 2014; Jianfeng Gao & Deng, 2014), and it adds further evidence for the effectiveness of these methods.”