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arXiv 2017年

作者： Zhang, Ruqing Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Representing texts as fixed-length vectors is central to many language processing tasks. Most traditional methods build text representations based on the simple Bag-of-Words (BoW) representation, which loses the rich semantic relations between words. Recent advances in natural language processing have shown that semantically meaningful representations of words can be efficiently acquired by distributed models, making it possible to build text representations based on a better foundation called the Bag-of-Word-Embedding (BoWE) representation. However, existing text representation methods using BoWE often lack sound probabilistic foundations or cannot well capture the semantic relatedness encoded in word vectors. To address these problems, we introduce the Spherical Paragraph Model (SPM), a probabilistic generative model based on BoWE, for text representation. SPM has good probabilistic interpretability and can fully leverage the rich semantics of words, the word co-occurrence information as well as the corpus-wide information to help the representation learning of texts. Experimental results on topical classification and sentiment analysis demonstrate that SPM can achieve new state-of-the-art performances on several benchmark datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Sentiment analysis

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MatchZoo: A toolkit for deep text matching

arXiv

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arXiv 2017年

作者： Fan, Yixing Pang, Liang Hou, JianPeng Guo, Jiafeng Lan, Yanyan Cheng, Xueqi Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods. In this paper, we introduce the MatchZoo toolkit that aims to facilitate the designing, comparing and sharing of deep text matching models. Specifically, the toolkit provides a unified data preparation module for different text matching problems, a flexible layer-based model construction process, and a variety of training objectives and evaluation metrics. In addition, the toolkit has implemented two schools of representative deep text matching models, namely representation-focused models and interaction-focused models. Finally, users can easily modify existing models, create and share their own models for text matching in MatchZoo. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

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A commentary of Digital contact tracing in MIT technology Review 2021

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Fundamental Research 2021年第6期1卷 838-839页

作者： Xueqi Cheng CAS Key Laboratory of Network Data Science and Technology Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China

In 2020,the COVID-19 pandemic has brought“digital contact tracing”to the forefront of public *** the context of COVID-19,technology has offered public health investigators a new capability for locating infected individuals,i.e.,digital contact *** this technology,investigators were able to track the location of patients without relying on their memory,which alleviated disease surveillance *** practical application of this technology is known as“Exposure Notification.”Developers were able to complete the creation and operation of this digital contact tracing system within a few weeks,and they made the code open-source to ensure that Apple and Android users worldwide could utilize it.

关键词： Developer Android individual

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Enhanced Semantic Head for cascade Instance Segmentation

Enhanced Semantic Head for Cascade Instance Segmentation

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Huang, Xuerong Su, Li Li, Guorong Zhang, Xinfeng Qing, Laiyun Huang, Qingming University of Chinese Academy of Sciences Beijing China Cas Key Lab of Big Data Mining and Knowledge Management Beijing China Institute of Computing Technology Cas Key Lab of Intelligent Information Processing Beijing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Recently, cascade instance segmentation inspired by cascade object detection has achieved notable performance. Due to the lack of global information, many methods suffer from incomplete segmentation such as missing edge regions and discontinuities within instances. To solve this problem, we proposed an effective and flexible semantic head to extract enhanced spatial context information. A vision transformer is utilized to generate global context features, and a convolution network is adopted to generate spatial context features. After combining the two modules, we obtain enhanced semantic segmentation features for segmentation. Extensive experiments show that the enhanced semantic head achieves 40.6% and 42.3% mask AP for cascade predictor HTC and DSC, which surpass about 0.9 and 1.4 percentage points respectively. The enhanced semantic head is universal and effective to improve the performance of different cascade predictors. © 2022 IEEE.

关键词： Object detection

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Quantum-to-quantum Bernoulli factory problem

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Physical Review A 2018年第3期97卷 032303-032303页

作者： Jiaqing Jiang Jialin Zhang Xiaoming Sun CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Given a coin with unknown bias p∈[0,1], can we exactly simulate another coin with bias f(p)? The exact set of simulable functions has been well characterized 20 years ago. In this paper, we ask the quantum counterpart of this question: Given the quantum coin |p〉=p|0〉+1−p|1〉, can we exactly simulate another quantum coin |f(p)〉=f(p)|0〉+1−f(p)|1〉? We give the full characterization of simulable quantum state k0(p)|0〉+k1(p)|1〉 from quantum coin |p〉=p|0〉+1−p|1〉, and present an algorithm to transform it. Surprisingly, we show that simulable sets in the quantum-to-quantum case and classical-to-classical case have no inclusion relationship with each other.

关键词： Quantum algorithms Quantum computation Quantum simulation

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Continuous Distributed Processing of Software Defined Radar

Continuous Distributed Processing of Software Defined Radar

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2021 CIE International Conference on Radar, Radar 2021

作者： Li, Bing Qiu, Qiang Gong, Shiqi Liu, Yongjun Lei, Yu Institute of Computing Technology CAS Key Lab of Network Data Science and Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Xidian University National Laboratory of Radar Signal Processing Xi'an China Golaxy Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665498142

Software-defined radar has been an active research field for more than ten years. However, the low performance and low scalability of the traditional processing techniques of SDR make it hard to deal with complex radar tasks. The stream processing engine is a software that supports low latency and high scalability distributed data processing. It is widely used in log, image, and video processing scenarios but rarely used in radar applications. This paper reviews processing software-defined radar data by using the stream processing engine, and we illustrate the differences between processing log data and radar data. Furthermore, we discuss some optimizations for stream processing engines to get better performance in the radar data processing. © 2021 IEEE.

关键词： distributed processing Software-defined radar stream processing engine

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BaKGraSTeC: A Background Knowledge Graph Based Method for Short Text Classification

BaKGraSTeC: A Background Knowledge Graph Based Method for Sh...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Xuhui Jiang Yinghan Shen Yuanzhuo Wang Xiaolong Jin Xueqi Cheng CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Short text classification is an important task in the area of natural language processing. Recent studies attempt to employ external knowledge to improve classification performance, but they ignore the correlation between external knowledge and have poor interpretability. This paper proposes a novel Background Knowledge Graph based method for Short Text Classification called BaKGraSTeC for short, which can not only employ external knowledge from a knowledge graph to enrich text information, but also utilize its structural information through a graph neural network to promote the understanding of texts. Specifically, we construct a background knowledge graph based on training data, then we propose a novel architecture that integrates background knowledge graph into a graph neural network to model and capture implicit interactions between its concepts and classes. Besides, we propose an attention mechanism considering both similarity and co-occurrence between concepts and classes to identify the informative concepts in texts. Our experimental results demonstrate the effectiveness with good interpretability of BaKGraSTeC through using external knowledge and their structural information for short text classification.

关键词： Knowledge engineering Neural networks Task analysis Semantics Natural language processing Syntactics Machine learning

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Parametric local multimodal hashing for cross-view similarity search

Parametric local multimodal hashing for cross-view similarit...

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23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

作者： Zhai, Deming Chang, Hong Zhen, Yi Liu, Xianming Chen, Xilin Gao, Wen School of Computer Science and Technology Harbin Institute of Technology Harbin China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781577356332

Recent years have witnessed the growing popularity of hashing for efficient large-scale similarity search. It has been shown that the hashing quality could be boosted by hash function learning (HFL). In this paper, we study HFL in the context of multimodal data for cross-view similarity search. We present a novel multimodal HFL method, called Parametric Local Multimodal Hashing (PLMH), which learns a set of hash functions to locally adapt to the data structure of each modality. To balance locality and computational efficiency, the hashing projection matrix of each instance is parameterized, with guaranteed approximation error bound, as a linear combination of basis hashing projections of a small set of anchor points. A local optimal conjugate gradient algorithm is designed to learn the hash functions for each bit, and the overall hash codes are learned in a sequential manner to progressively minimize the bias. Experimental evaluations on cross-media retrieval tasks demonstrate that PLMH performs competitively against the state-of-the-art methods.

关键词： Hash functions

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A tree search algorithm for sequence labeling

arXiv

引用

arXiv 2018年

作者： Lao, Yadi Xu, Jun Lan, Yanyan Guo, Jiafeng Gao, Sheng Cheng, Xueqi Guo, Jun Beijing University of Posts and Telecommunications CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Science

In this paper we propose a novel reinforcement learning based model for sequence tagging, referred to as MM-Tag. Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from le. to right, and each action corresponds to assign a tag to a word. Two long short-term memory networks (LSTM) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of LSTMs, the policy for guiding the tag assignment and the value for predicting the whole tagging accuracy of thewhole sentence are produced.e policy and value are then strengthened with MCTS, which takes the produced raw policy and value as inputs, simulates and evaluates the possible tag assignments at the subsequent positions, and outputs a *** search policy for assigning tags. A reinforcement learning algorithm is proposed to train the model parameters. Our work is the first to apply the MCTS enhanced MDP model to the sequence tagging task. We show that MM-Tag can accurately predict the tags thanks to the exploratory decision making mechanism introduced by MCTS. Experimental results show based on a chunking benchmark showed that MM-Tag outperformed the state-of-the-art sequence tagging baselines including CRF and CRF with LSTM. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

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On the Capacity of Citation Generation by Large Language Models

arXiv

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arXiv 2024年

作者： Qian, Haosheng Fan, Yixing Zhang, Ruqing Guo, Jiafeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Retrieval-augmented generation (RAG) appears as a promising method to alleviate the "hallucination" problem in large language models (LLMs), since it can incorporate external traceable resources for response generation. The essence of RAG in combating the hallucination issue lies in accurately attributing claims in responses to the corresponding retrieved documents. However, most of existing works focus on improving the quality of generated responses from the LLM, while largely overlooked its ability to attribute sources accurately. In this study, we conduct a systematic analysis about the capabilities of LLMs in generating citations within response generation, and further introduce a novel method to enhance their citation generation abilities. Specifically, we evaluate both the correctness and citation quality for seven widely-used LLMs on two benchmark datasets. Meanwhile, we introduce new citation evaluation metrics to eliminate the over-penalization of unnecessary and excessive citations in existing metrics. Furthermore, we propose a Generate-then-Refine method that completes relevant citations and removes irrelevant ones without altering the response text. The results on WebGLM-QA, ASQA and ELI5 datasets show that our method substantially improves the quality of citations in responses generated by LLMs. Copyright © 2024, The Authors. All rights reserved.

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