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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xin Sun Xiangyu Ren Xiaohao Xie School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Applications Research Center on High Volume Language Information Processing and Cloud Computing

Sentiment analysis is an important research area in Natural language processing (NLP). With the explosion of multimodal data, Multimodal Sentiment Analysis (MSA) attracts more and more attention in recent years. How to Effectively harnessing the interplay between diverse modalities is paramount to achieving comprehensive fusion of MSA. However, current research predominantly emphasizes modality interaction, while overlooking unimodal information, thus neglecting the inherent disparities between modalities. To address these issues, we propose a novel model for multimodal sentiment analysis based on gated fusion and multi-task learning. The model adopts multi-task learning to concurrently address both multimodal and unimodal sentiment analysis tasks. Specifically, for the multimodal task, we leverage cross-modal Transformers with gating mechanisms to facilitate modality fusion. Subsequently, the fused representations are harnessed to generate sentiment labels for the unimodal tasks. Experiments on the CMU-MOSI and CMU-MOSEI datasets demonstrate that our model outperforms the existing methods and achieves the state-of-the art performance.

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Microblog Keyphrase Extraction Based on Similarity Features

Microblog Keyphrase Extraction Based on Similarity Features

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2013 International Conference on Advanced Computer Science and Electronics information(ICACSEI 2013)

作者： Lizi Liao Heyan Huang Beijing Engineering Applications Research Center of High Volume Language Information Processing and Cloud Computing (BIT) School of Computer Science and Technology Beijing Institute of Technology

This paper proposes to extract keyphrases from microblog based on similarity features. We analyze a large number of microblogs and find an interesting phenomenon that people use various nugget phrases to express the same factoid while many of these nugget phrases show similarity relationships. We propose a similarity features based context-sensitive topical PageRank method for keyphrase ranking. We evaluate our proposed methods on a large microblog dataset. Experiments show that our system is very effective for keyphrase extraction.

关键词： Keyphrase extraction, Similarity features, Graph- based model

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PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

arXiv

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arXiv 2023年

作者： Sun, Huashan Wu, Yixiao Ye, Yuhao Yang, Yizhe Li, Yinghao Li, Jiawei Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

language style is necessary for AI systems to understand and generate diverse human language accurately. However, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application needs. To overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking style. Grounded in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of LLMs. For such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized texts. Comprehensive experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Concolic Execute Fuzzing Based on Control-Flow Analysis

Concolic Execute Fuzzing Based on Control-Flow Analysis

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International Conference on Computational Intelligence and Security

作者： Jingxi Li Xin Xu Lejian Liao Lu Li Beijing Engineering Research Center of High Volume Language Information Processing & Cloud Computing Applications Beijing Institute of Technology Beijing China DISECC Beijing China

ISBN: (纸本)9781467386616

This paper proposes a method which utilizing taint analysis to reduce the unnecessary analysis routine, concentrating on the control-flow altering input using concolic (concrete and symbolic) execution procedure. A prototype, Concolic Fuzz is implemented based on this method, which is built on Pin platform at x86 binary level and using Z3 as the SMT (Satisfiability Modulo Theories) solver. The results of experiments verify that our approach is effective in increasing code coverage with remarkably lower resource and time cost than the standard fuzzing and concolic testing tools. The scale of fuzzing range and symbols are reduced, so as the computing resource and time consumption, especially when the input data is in highly structured and complex file format.

关键词： Instruments Security Software Testing Registers Concrete Performance analysis

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Why Not Look One Step Ahead in Reinforcement Learning Based Knowledge Graph Reasoning?

SSRN

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SSRN 2024年

作者： Wang, Hao Song, Dandan Wu, Zhijing Tian, YuHang Xu, Jing Laboratory of High-Volume Language Information Processing and Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

Multi-hop reasoning is an effective and explainable method for query answering to improve interpretability by finding reasoning paths over knowledge graphs (KGs). Recent studies apply reinforcement learning-based (RL-based) methods with policy agents to solve this problem. However, they only focus on the sequential reasoning paths and the candidate nodes regardless of the local information of adjacency subgraphs and the semantic correlations between relations in every decision step. In this paper, we propose an RL-based multi-hop reasoning model, Look One Step Ahead (LOSA), which regards more expressive adjacency subgraphs rather than nodes as candidate actions and pays attention to the semantic correlations between relations. Specifically, an adjacency aggregation module encodes the information of subgraphs and transmits the encoding to the policy network to help make decisions, which can reduce backtracking and alleviate sparse-reward issues. Furthermore, a semantic matching module is designed to stress the semantic correlations of relations to improve the rationality of the reasoning paths. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed approach. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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A hierarchy-based constraint dependency grammar parsing for Chinese

A hierarchy-based constraint dependency grammar parsing for ...

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International Conference on Audio, language and Image processing, ICALIP

作者： Peng Li Lejian Liao Xin Li Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications School of Computer Science Beijing Institute of Technology Beijing China

Constraint Dependency Grammar (CDG) is a well known formalism which represents the grammatical rules as constraints on dependency relations. It reduces the structural ambiguities via constraint-propagation instead of generating individual parse trees as the conventional works. In our previous work, we have successfully adopted CDG in Chinese [5]. In this paper, we tend to further explore the nature of the constraints for Chinese CDG. We propose to develop a schema of three hierarchies for the constraints, namely in-the-phrase, between-the-phrase and between-the-simple-sentence, which is based on a study of constraints in corpus with a prior knowledge. With this proposed schema, we could further diminish the ambiguities caused by the originally defined constraints which is lack of hierarchical grammar information considered, The experiments are conducted and the results support our claims.

关键词： Grammar Syntactics Filtering information processing Educational institutions Semantics Presses

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Earlier attention? Aspect-aware LSTM for aspect-based sentiment analysis

arXiv

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arXiv 2019年

作者： Xing, Bowen Liao, Lejian Song, Dandan Wang, Jingang Zhang, Fuzhen Wang, Zhongyuan Huang, Heyan Lab of High Volume Language Information Processing & Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology Meituan-Dianping Group

Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be improved to generate more effective context representations. This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism. Therefore, our AA-LSTM can dynamically produce aspect-aware contextual representations. We experiment with several representative LSTM-based models by replacing the classic LSTM cells with the AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the effectiveness of AA-LSTM. Copyright © 2019, The Authors. All rights reserved.

关键词： Sentiment analysis

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An Association Rule Mining Algorithm for Clinical Decision Support 22

An Association Rule Mining Algorithm for Clinical Decision S...

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8th International Conference on computing and Artificial Intelligence, ICCAI 2022

作者： Cui, Jiaming Zhao, Shuo Sun, Xin School of Computer Science Fudan University Shanghai200433 China School of Computer Science and Technology Beijing Institue of Technology Beijing100081 China Beijing Eng. Res. Ctr. of High Vol. Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781450396110

The use of Machine Learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support. Association Rule Mining (ARM), as a classical machine learning method, provides researchers with important opportunities for data analysis and potential relationship discovery. In this paper, the weighted Apriori algorithm (MW-Apriori) is proposed to discovery the association rules from the disease diagnostic data. Furthermore, in order to obtain more accurate associations on a higher level, we design an improved k-nearest neighbor (CW-KNN) algorithm applied as a pre-step, which can divide the large number of medical texts into specific classification. MW-Apriori, can mine the hidden and potentially useful knowledge and rules behind huge amount of uncertain information. Based on the high correlation of classified data, we have also obtained high-quality association rules between symptoms and diseases. Experiment results on the multiple data sets have proved the superior performance of MW-Apriori algorithm. © 2022 ACM.

关键词： Association rules

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Momentum Decoding: Open-ended Text Generation As Graph Exploration

arXiv

引用

arXiv 2022年

作者： Lan, Tian Su, Yixuan Liu, Shuhang 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

Open-ended text generation with autoregressive language models (LMs) is one of the core tasks in natural language processing. However, maximization-based decoding methods (e.g., greedy/beam search) often lead to the degeneration problem, i.e., the generated text is unnatural and contains undesirable repetitions. Existing solutions to this problem either introduce randomness prone to incoherence or require a look-ahead mechanism that demands extra computational overhead. In this study, we formulate open-ended text generation from a new perspective, i.e., we view it as an exploration process within a directed graph. Thereby, we understand the phenomenon of degeneration as circular loops within the directed graph. Based on our formulation, we propose a novel decoding method—momentum decoding—which encourages the LM to greedily explore new nodes outside the current graph. Meanwhile, it also allows the LM to return to the existing nodes with a momentum downgraded by a pre-defined resistance function. We extensively test our approach on three benchmarks from different domains through automatic and human evaluations. The results show that momentum decoding performs comparably with the current state of the art while enjoying notably improved inference speed and computation FLOPs. Furthermore, we conduct a detailed analysis to reveal the merits and inner workings of our approach. © 2022, CC BY.

关键词： Momentum

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Design and Optimization of a Big Data computing Framework Based on CPU/GPU Cluster

Design and Optimization of a Big Data Computing Framework Ba...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Yanlong Zhai Ying Guo Qiurui Chen Kai Yang Emmanuel Mbarushimana Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China Beijing Simulation Center Beijing China

Big data processing is receiving significant amount of interest as an important technology to reveal the information behind the data, such as trends, characteristics, etc. MapReduce is one of the most popular distributed parallel data processing framework. However, some high-end applications, especially some scientific analyses have both data-intensive and computation intensive features. Therefore, we have designed and implemented a high performance big data process framework called Lit, which leverages the power of Hadoop and GPUs. In this paper, we presented the basic design and architecture of Lit. More importantly, we spent a lot of effort on optimizing the communications between CPU and GPU. Lit integrated GPU with Hadoop to improve the computational power of each node in the cluster. To simplify the parallel programming, Lit provided an annotation based approach to automatically generate CUDA codes from Hadoop codes. Lit hid the complexity of programming on CPU/GPU cluster by providing extended compiler and optimizer. To utilize the simplified programming, scalability and fault tolerance benefits of Hadoop and combine them with the high performance computation power of GPU, Lit extended the Hadoop by applying a GPUClassloader to detect the GPU, generate and compile CUDA codes, and invoke the shared library. For all CPU-GPU co-processing systems, the communication with the GPU is the well-known performance bottleneck. We introduced data flow optimization approach to reduce unnecessary memory copies. Our experimental results show that Lit can achieve an average speedup of 1× to 3× on three typical applications over Hadoop, and the data flow optimization approach for the Lit can achieve about 16% performance gain.

关键词： Graphics processing units Optimization Programming Java Kernel Computer architecture Data processing

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