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Tag recommendation by word-level tag sequence modeling

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arXiv 2019年

作者： Shi, Xuewen Huang, Heyan Zhao, Shuyang Jian, Ping Tang, Yi-Kun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

Recently, neural network based tag recommendation has gained much attention. Most of these methods regard tag recommendation as multi-label classification. They treat different tags as individual categories with fixed labels, without considering the rich relations among tags. Moreover, using a fixed number of categories cannot tackle dynamic, changing tags with ongoing topics. In this paper, we transform tag recommendation into a word-based text generation problem and introduce a sequence-to-sequence model. For efficiently modeling the semantic dependencies among tags in tag sequence and the strong sequential relations among the tag-words, we propose an essential sequence-to-sequence model, named LSTM-Attention. The model inherits the advantages of recurrent network based encoder for sequential modeling and attention based decoder for learning relations globally. In addition, as a text generation method, the proposed model is able to generate unseen tags, which is more applicable and flexible to real scenarios. Extensive experimental results on two datasets, i.e., Zhihu and Weibo, clearly illustrate the proposed model significantly outperforms other state-of-the-art text classification based methods and well demonstrate its advantage of handling unseen tags. Copyright © 2019, The Authors. All rights reserved.

关键词： Text processing

Web Question Answering based on CCG parsing and DL ontology

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Web Question Answering based on CCG parsing and DL ontology

International Conference on information Science and Digital Content Technology

作者： Peng Li Lejian Liao Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications Multi-living-agent Information System Key Laboratory and Beijing Laboratory of Intelligent Information Technology School of Computer Scienc

ISBN: (纸本)9781467312882

This paper presents a novel WQA (Web Question Answering) approach based on the combination CCG (Combinatory Categorial Grammar) and DL (Description Logic) ontology, in order to promote semantic-level accuracy through deep text understanding capabilities. We propose to take DL based semantic modeling, i.e., translating lambda-expression encoding of question meaning into DL based semantic representations. The advantage of such approach is a seamless exploitation of existing semantic resource coded as DL ontology, which is widespread in such area as the Semantic Web and conceptual modeling. The experiments are conducted with a repository of complex Chinese questions which involves the satisfaction of some object property restrictions. The experimental results show that producing the semantic representations with the combination of CCG parsing and DL reasoning is an effective approach for question understanding at semantic level, in terms of both understanding accuracy promotion and semantic resource exploitation.

关键词： Semantics Ontologies Syntactics Grammar Cognition Accuracy Semantic Web

A privacy-preserving traffic monitoring scheme via vehicular crowdsourcing

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arXiv 2019年

作者： Zhang, Chuan Zhu, Liehuang Xu, Chang Du, Xiaojiang Guizani, Mohsen Beijing Eng. Res. Center of Massive Language Information Processing and Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States College of Engineering Qatar University

The explosive growth of vehicle amount has given rise to a series of traffic problems, such as traffic congestion, road safety, and fuel waste. Collecting vehicles' speed information is an effective way to monitor the traffic condition and avoid vehicles being congested, which however may bring threats to vehicles' location and trajectory privacy. Motivated by the fact that traffic monitoring does not need to know each individual vehicle's speed and the average speed would be sufficient, we propose a privacy-preserving traffic monitoring (PPTM) scheme to aggregate vehicles' speeds at different locations. In PPTM, the roadside unit (RSU) collects vehicles' speed information at multiple road segments, and further cooperates with a service provider to calculate the average speed information for every road segment. To preserve vehicles' privacy, both homomorphic Paillier cryptosystem and super-increasing sequence are adopted. A comprehensive security analysis indicates that the proposed PPTM can preserve vehicles' identities, speeds, locations, and trajectories privacy from being disclosed. In addition, extensive simulations are conducted to validate the effectiveness and efficiency of the proposed PPTM scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Speed

Neural Chinese word segmentation as sequence to sequence translation

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arXiv 2019年

作者： Shi, Xuewen Huang, Heyan Jian, Ping Guo, Yuhang Wei, Xiaochi Tang, Yikun Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

Recently, Chinese word segmentation (CWS) methods using neural networks have made impressive progress. Most of them regard the CWS as a sequence labeling problem which construct models based on local features rather than considering global information of input sequence. In this paper, we cast the CWS as a sequence translation problem and propose a novel sequence-to-sequence CWS model with an attention-based encoder-decoder framework. The model captures the global information from the input and directly outputs the segmented sequence. It can also tackle other NLP tasks with CWS jointly in an end-to-end mode. Experiments on Weibo, PKU and MSRA benchmark datasets show that our approach has achieved competitive performances compared with state-of-the-art methods. Meanwhile, we successfully applied our proposed model to jointly learning CWS and Chinese spelling correction, which demonstrates its applicability of multi-task fusion. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

Concept pointer network for abstractive summarization

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arXiv 2019年

作者： Wenbo, Wang Yang, Gao Heyan, Huang Yuxiang, Zhou School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distantly-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model's abstractive abilities also supports the quality of the summaries produced within this *** Codes 68U15, 68T50 Copyright © 2019, The Authors. All rights reserved.

关键词： Knowledge based systems

SciMRC: Multi-perspective Scientific Machine Reading Comprehension 30

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SciMRC: Multi-perspective Scientific Machine Reading Compreh...

Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China State Grid Smart Grid Research Institute Co. Ltd. China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9782493814104

Scientific Machine Reading Comprehension (SMRC) aims to facilitate the understanding of scientific texts through human-machine interactions. While existing dataset has significantly contributed to this field, it predominantly focus on single-perspective question-answer pairs, thereby overlooking the inherent variation in comprehension levels among different readers. To address this limitation, we introduce a novel multi-perspective scientific machine reading comprehension dataset, SciMRC, which incorporates perspectives from beginners, students, and experts. Our dataset comprises 741 scientific papers and 6,057 question-answer pairs, with 3,306, 1,800, and 951 pairs corresponding to beginners, students, and experts respectively. Extensive experiments conducted on SciMRC using pre-trained models underscore the importance of considering diverse perspectives in SMRC and highlight the challenging nature of our scientific machine comprehension tasks. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

Dynamic Regret with Unknown Delays

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SSRN

SSRN 2023年

作者： Wu, Ping Liu, Zhengyang Lu, Haolin Huang, Heyan School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

We study the problem of online convex optimization in dynamic environments with unknown and arbitrary delays. Without delays, existing work has achieved the optimal $\Theta(\sqrt{(1+\Delta_T)T})$ regret bound for any given sequence of comparators, where $T$ is the time horizon and $\Delta_T$ is the path-length of the comparator sequence. However, their algorithm assumes that the loss function can be revealed immediately to the agent after making the decision. In this paper, we propose a novel algorithm to tackle the delayed setting. We show that it enjoys $\mathcal{O}(\max\{\sqrt{(1+\Delta_T)D_T},(\hat{d}-1)\Delta_T\})$ regret bound, where $\hat{d}$ is the maximum delay and $D_T$ is the cumulative delay over the whole horizon. To complement our algorithm, we also establish an $\Omega(\sqrt{(1+\Delta_T)D_T})$ lower bound. The basic idea is to employ a delayed meta algorithm to track parallel delayed expert algorithms with varying step sizes. We further extend our algorithm to the case that we have no idea about the time horizon and cumulative delay using a novel doubling trick. Finally, numerical simulations are provided to illustrate the validity of our theoretical results. © 2023, The Authors. All rights reserved.

关键词： Comparators (optical)

MindLLM: Pre-training Lightweight Large language Model from Scratch, Evaluations and Domain applications

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arXiv 2023年

作者： Yang, Yizhe Sun, Huashan Li, Jiawei Liu, Runheng Li, Yinghao Liu, Yuhang Gao, Yang Huang, Heyan School of Computer Science Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Southeast Academy of Information Technology China

Large language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence. While general artificial intelligence is leveraged by developing increasingly large-scale models, there could be another branch to develop lightweight custom models that better serve certain domains, taking into account the high cost of training and deploying LLMs and the scarcity of resources. In this paper, we present MindLLM, a novel series of bilingual lightweight large language models, trained from scratch, alleviating such burdens by offering models with 1.3 billion and 3 billion parameters. A thorough account of experiences accrued during large model development is given, covering every step of the process, including data construction, model architecture, evaluation, and applications. Such insights are hopefully valuable for fellow academics and developers. MindLLM consistently matches or surpasses the performance of other open-source larger models on some public benchmarks. We also introduce an innovative instruction tuning framework tailored for smaller models to enhance their capabilities efficiently. Moreover, we explore the application of MindLLM in specific vertical domains such as law and finance, underscoring the agility and adaptability of our lightweight models. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

Contracting of E-Business Services in a Constraint- Based Process Modeling Framework

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Contracting of E-Business Services in a Constraint- Based Pr...

The Seventh International Conference on Semantics,Knowledge,and Grids(第七届语义、知识与网格国际会议 SKG 20110)

作者： Ruoyu Chen Lejian Liao Zhi Fang Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications Beijing Laboratory of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing 100081 Chi

Analyzing and contracting of E-Business service processes is a crucial step between discovery and execution of Semantic Web Services. This paper proposes a service modelling framework consists of constraint-based service contract, agent style communication messages and service process specification. Service contract is used as shared knowledge between participants and instrument for advertising and requesting of services. The Constraint Process Specification proposed is capable of describing complex data and control-flow restrictions as well as data content restrictions. A state transition based operational semantic for this framework is given. A virtual machine is built for analyzing and contracting of services. We illustrate the expressiveness of this framework within an online shopping scenario.

关键词： Semantic Web Service Service Contracting Constraint Process Specification