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LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

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Computers, Materials & Continua 2024年第6期79卷 4283-4296页

作者： Hao Chen Runfeng Xie Xiangyang Cui Zhou Yan Xin Wang Zhanwei Xuan Kai Zhang State key laboratory of Communication content Cognition People’s Daily OnlineBeijing100733China

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation *** methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation ***,these traditional methods are more friendly to active users with rich historical ***,they can not effectively solve the long tail problem of inactive *** address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional *** learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional *** addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail *** results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation *** successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news *** code is available at https://***/Xuan-ZW/LKPNR.

关键词： Large language models news recommendation knowledge graphs(KG)

Agglutinative Language speech Recognition Using Automatic Allophone Deriving

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Chinese Journal of Electronics 2016年第2期25卷 328-333页

作者： XU Ji PAN Jielin YAN Yonghong The key laboratory of speech acoustics and content understanding Chinese Academy of Sciences

Agglutinative language involves agglutination extensively, which results in the significant pronunciation variations in different contexts. Therefore, it is a problem to use phoneme sets translated from their written forms as basic units for acoustic modeling, due to the incapability to capture the pronunciation variations in Large-vocabulary continuous speech recognition(LVCSR).This paper presented a novel approach called Automatic allophone deriving(AAD) to create allophone candidates without any linguistic prior knowledge. Furthermore, an enhanced approach AAD-LT is proposed in which longtime features are used in AAD approach. Experiments are conducted on three languages which contains two agglutinative ones and an analytic one. The experiments suggest that AAD Long-time(AAD-LT) is very effective for the agglutinative languages in which more than 10% relative CER reduction is obtained.

关键词： Agglutinative language Large-vocabulary continuous speech recognition(LVCSR) Allophone Unsupervised

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Journal of Computer Science & Technology 2012年第2期27卷 443-450页

作者：李亚丽徐为群颜永红 key laboratory of speech acoustics and content understanding Chinese Academy of Sciences

In this paper,we propose a novel co-occurrence probabilities based similarity measure for inducing semantic *** with the new similarity measure outperforms the widely used distance based on Kullback-Leibler divergence in precision,recall and F1 *** our experiments,we induced semantic classes from unannotated in-domain corpus and then used the induced classes and structures to generate large in-domain corpus which was then used for language model *** recognition rate was improved from 85.2% to 91%.We imply a new measure to solve the lack of domain data problem by first induction then generation for a dialogue system.

关键词： semantic class induction lack of domain data language model adaptation

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A General Bayesian Model for Speaker Verification

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Chinese Journal of Electronics 2016年第6期25卷 1045-1051页

作者： XU Yunfei YANG Hai YANG Lin ZHOU Ruohua YAN Yonghong key laboratory of speech acoustics and content understanding Institute of AcousticsChinese Academy of Sciences

This paper presents a general Bayesian model for speaker verification tasks. It is a generative probability model. Due to its simple analytical property, a computationally efficient expectation-maximization algorithm can be derived to obtain the model parameters. A closedform solution, which allows the scalable size of enrollment set, is given in a full Bayesian way for making speaker verification decisions. Factor analysis technique is employed to model the speaker-specific components, then the redundant information in this model will be dropped. Experimental results are evaluated by both equal error rate and minimum detection cost function. The proposed approach shows promising results on the National institute of standards and technology(NIST) Speaker recognition evaluation(SRE) 2010 extended and 2012 core tasks. Significant improvement is obtained when comparing with Gaussian probabilistic linear discriminant analysis, especially under phone-call conditions and mismatched train-test channel conditions. Contrast experimental results with other popular generative probability models are also presented in this paper.

关键词： Bayesian model Speaker verification I-Vector

speech Enhancement Using Multi-channel Post-Filtering with Modified Signal Presence Probability in Reverberant Environment

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Chinese Journal of Electronics 2016年第3期25卷 512-519页

作者： WANG Xiaofei GUO Yanmeng FU Qiang YAN Yonghong key laboratory of speech acoustics and content understanding Institute of AcousticsChinese Academy of Sciences

In this paper, a multi-channel post-filtering approach in reverberant environment based on detection and estimation scheme is presented. A modified Signal presence probability(SPP), which is in consideration of reverberation, is proposed with a novel estimator Direct-toreverberate ratio(DRR) to adapt to distant-talking *** is known a key estimator to instruct the updating of transient noise or residual directional interference and form gain function in the time-frequency domain, consequently a new desired signal detection scheme is proposed to improve its accuracy. Appropriate spectral enhancement technique is applied to the noisy speech signal taking advantage of the modified SPP estimator. The proposed multi-channel post-filtering is tested in different nonstationary noisy and reverberant environments. Experimental results show that it achieves considerable improvement on signal preservation of the desired speech with more noise reduction over the comparative algorithms.

关键词： speech enhancement Multi-channel post-filtering Reverberation robust Signal detection

Automatic Piano Music Transcription Using Audio-Visual Features

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Chinese Journal of Electronics 2015年第3期24卷 596-603页

作者： WAN Yulong WANG Xianliang ZHOU Ruohua YAN Yonghong The key laboratory of speech acoustics and content understanding Chinese Academy of Sciences

The performance of automatic music transcription seems to have reached a limit over the last decade,and a promising direction of improvements could be to incorporate music instruments' specific parameters. We propose a novel piano-specific transcription system, using both audio and visual features for the first time. Contribution of the paper mainly includes two parts: A new onset detection method is proposed using a specific spectrum envelope matched filter on multiple frequency bands. A computer-vision method is proposed to enhance audio-only piano music transcription, through tracking the positions of the pianist's hands on the piano keyboard. Based on the MIDI Aligned piano sounds(MAPS) database and a selfrecorded video database, we carried out comparable experiments for audio-only onset detection and overall system,respectively. The performance was compared with the best piano transcription system in Music information retrieval evaluation exchange(MIREX), and the results showed that the proposed system outperforms the state-of-art method substantially.

关键词： Music transcription Polyphonic piano Onset detection Matched filtering Multimedia fusion

Using Highway Connections to Enable Deep Small-footprint LSTM-RNNs for speech Recognition

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Chinese Journal of Electronics 2019年第1期28卷 107-112页

作者： CHENG Gaofeng LI Xin YAN Yonghong key laboratory of speech acoustics and content understanding Institute of Acoustics University of Chinese Academy of Sciences

Long short-term memory RNNs(LSTMRNNs) have shown great success in the Automatic speech recognition(ASR) field and have become the state-ofthe-art acoustic model for time-sequence modeling ***, it is still difficult to train deep LSTM-RNNs while keeping the parameter number small. We use the highway connections between memory cells in adjacent layers to train a small-footprint highway LSTM-RNNs(HLSTM-RNNs), which are deeper and thinner compared to conventional LSTM-RNNs. The experiments on the Switchboard(SWBD) indicate that we can train thinner and deeper HLSTM-RNNs with a smaller parameter number than the conventional 3-layer LSTM-RNNs and a lower Word error rate(WER) than the conventional *** with the counterparts of small-footprint LSTMRNNs, the small-footprint HLSTM-RNNs show greater reduction in WER.

关键词： Long short-term memory Highway connections Small-footprint speech recognition

Vision-based bio-inspired guidance law for small aerial vehicle

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Chinese Journal of Aeronautics 2015年第1期28卷 260-267页

作者： Wang Zhengjie Huang Weilin Yan Yonghong School of Mechatronical Engineering Beijing Institute of Technology key lab of speech acoustics and content understanding Institute of Acoustics Chinese Academy of Sciences

During predation, a flying insect can form a stealth flight path. This behavior is called motion camouflage. Based on the study results of this behavior, the perception and neurology of flying insects, a novel bio-inspired guidance law is proposed for the terminal guidance for small aerial vehicle with charge-coupled device imaging seekers. The kinematics relationship between a small aerial vehicle and target is analyzed, and a two-dimensional guidance law model is established by using artificial neural networks. To compare with the proportional guidance law, the numerical simulations are carried out in the vertical plane and in the horizontal plane respectively. The simulation results show that the ballistic of the small aerial vehicle is straighter and the normal acceleration is smaller by using the bio-inspired guidance law than by using the proportional guidance law. That is to say, the bio-inspired guidance law just uses the information of the target from the imaging seeker,but the performance of it can be better than that of the proportional guidance law.

关键词： guidance aerial inspired kinematics flying proportional retina acceleration capture trajectory

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A Prefix Tree Based n-best List Re-scoring Strategy for Recurrent Neural Network Language Model

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Chinese Journal of Electronics 2014年第1期23卷 70-74页

作者： SI Yujing LI Ta PAN Jielin YAN Yonghong The key laboratory of speech acoustics and content understanding Chinese Academy of Sciences

In this paper, issues of speeding up Recurrent neural network language model(RNNLM) in the testing phase are explored so that RNNLMs can be used to re-rank a large n-best list in real-time systems which could obtain better performance. A new n-best list rescoring framework, Prefix tree based n-best list re-scoring(PTNR), is proposed to hundred percent eliminate the repeated computations which makes n-best list re-scoring ineffective. At the same time, the bunch mode technique,widely-used in speeding up the training of Feed-forward neural network language model(FF-NNLM), is combined with PTNR and the speed is further improved. Experimental results show that our approach is much faster than basic n-best list re-scoring. Take 1000-best as an example, our approach is almost 11 times faster than the basic n-best list re-scoring.

关键词： Recurrent neural network language model(RNNLM) n-best list Prefix tree based n-best list re-scoring(PTNR) Speedup Bunch mode