Filters








9,295 Hits in 6.3 sec

Transferring topical knowledge from auxiliary long texts for short text clustering

Ou Jin, Nathan N. Liu, Kai Zhao, Yong Yu, Qiang Yang
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
In this article, we present a novel approach to cluster short text messages via transfer learning from auxiliary long text data.  ...  We show that while some previous works for enhancing short text clustering with related long texts exist, most of them ignore the semantic and topical inconsistencies between the target and auxiliary data  ...  In this work, we attempt to transfer the topical knowledge from the auxiliary long texts to help with the unsupervised learning task on target short texts.  ... 
doi:10.1145/2063576.2063689 dblp:conf/cikm/JinLZYY11 fatcat:vrgl4lrwfrdzppm3j7doebkdnu

Transferring auxiliary knowledge to enhance heterogeneous web service clustering

Gang Tian, Chengai Sun, Ke qing He, Xiang min Ji
2016 International Journal of High Performance Computing and Networking  
To solve this problem, we propose a new service clustering approach based on transfer learning from auxiliary long text data obtained from Wikipedia.  ...  Most existing clustering approaches are designed to handle long text documents.  ...  DT-LDA The proposed DT-LDA model extends an augment LDA model ATM and transfers the topical knowledge from the auxiliary long texts to help the unsupervised learning task on target short texts.  ... 
doi:10.1504/ijhpcn.2016.074669 fatcat:5c5eei6ylfh25f7phicwew4pmu

FSFP: Transfer Learning From Long Texts to the Short

Wei Fengmei, Zhang Jianpei, Chu Yan, Yang Jing
2014 Applied Mathematics & Information Sciences  
It can transfer knowledge from the long texts to the short ones.  ...  However, the source data should be given for the transportation from long texts to the short ones, and the priori probability distribution of the data should be given at the same time.  ...  learning, Tr SVM which was used to transfer knowledge from the long texts to the long, and DLDA that could transfer from the long texts to the short.  ... 
doi:10.12785/amis/080462 fatcat:7pl62aqkynhaja6luvkrvgjmfm

Discovering Topic Representative Terms for Short Text Clustering

Shuiqiao Yang, Guangyan Huang, Borui Cai
2019 IEEE Access  
Clustering short texts are one of the most important text analysis methods to help extract knowledge from online social media platforms, such as Twitter, Facebook, and Weibo.  ...  This paper provides a novel topic representative term discovery (TRTD) method for short text clustering.  ...  For example, Wiki_Method enriches short text representation with additional features from Wikipedia; DLDA transfers topic relevant knowledge from auxiliary long texts to short texts for topic distribution  ... 
doi:10.1109/access.2019.2927345 fatcat:7jltjkmohzae5ha2wk5rlvblzi

Improving Document Clustering for Short Texts by Long Documents via a Dirichlet Multinomial Allocation Model [chapter]

Yingying Yan, Ruizhang Huang, Can Ma, Liyang Xu, Zhiyuan Ding, Rui Wang, Ting Huang, Bowei Liu
2017 Lecture Notes in Computer Science  
the number of clusters; 3) separates discriminative words from irrelevant words for long documents to obtain high quality structural knowledge.  ...  To better understand short texts, we observe that words that appear in long documents can enrich short text context and improve the clustering performance for short texts.  ...  The fourth approach, labeled as DLDA model, is the most recent short text clustering model which transfers structural knowledge learned from auxiliary long documents to short texts [9] .  ... 
doi:10.1007/978-3-319-63579-8_47 fatcat:eqkjrwpez5cfxdchzo4skwdltq

A Review of Text Corpus-Based Tourism Big Data Mining

Qin Li, Shaobo Li, Sen Zhang, Jie Hu, Jianjun Hu
2019 Applied Sciences  
We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism  ...  As an effective expression means of tourists' opinions, text mining of such data has big potential to inspire innovations for tourism practitioners.  ...  Using long text to assist short text by importing external-related information from Wikipedia and WordNet, etc.  ... 
doi:10.3390/app9163300 fatcat:chb3pbtj5jgq7fauniomsb22yu

Automatic transfer learning for short text mining

Lei Yang, Jianpei Zhang
2017 EURASIP Journal on Wireless Communications and Networking  
Most of the existing transfer learning methods are designed for long texts and short texts.  ...  A transfer learning algorithm called automatic transfer learning (AutoTL) is proposed for short text mining.  ...  Recently, some research has been conducted on the short text analysis by transferring the knowledge from the long texts. For example, Jin et al.  ... 
doi:10.1186/s13638-017-0815-5 fatcat:pd2lisq4ovdozaoi2v5cd756n4

Neural Text Classification by Jointly Learning to Cluster and Align [article]

Yekun Chai, Haidong Zhang, Shuo Jin
2020 arXiv   pre-print
We extend the neural text clustering approach to text classification tasks by inducing cluster centers via a latent variable model and interacting with distributional word embeddings, to enrich the representation  ...  Distributional text clustering delivers semantically informative representations and captures the relevance between each word and semantic clustering centroids.  ...  We find that models with three layers reach their acme on test performance for short texts whilst the test accuracy with layer numbers range from 3 to 7 remains a steady stage for long texts.  ... 
arXiv:2011.12184v1 fatcat:fsi2vft3y5fifkysf6omjawtvu

Combining Lexical and Semantic Features for Short Text Classification

Lili Yang, Chunping Li, Qiang Ding, Li Li
2013 Procedia Computer Science  
The experiment results show that our approach has better effectiveness compared with existing methods for classifying short texts.  ...  In this paper, we propose a novel approach to classify short texts by combining both their lexical and semantic features.  ...  [4] presented a novel approach to cluster short text messages via transfer learning from auxiliary longer textual data and applied the topic model, assumed that short texts and auxiliary texts have  ... 
doi:10.1016/j.procs.2013.09.083 fatcat:4w2mdyjadjawbhwbniaji36w7u

Short Text Topic Modeling Techniques, Applications, and Performance: A Survey [article]

Qiang Jipeng and Qian Zhenyu and Li Yun and Yuan Yunhao and Wu Xindong
2019 arXiv   pre-print
Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts  ...  Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts.  ...  [15] searched auxiliary long texts for short texts to infer latent topics of short texts for clustering.  ... 
arXiv:1904.07695v1 fatcat:3bdg62jyhncivc245nyx7v4tjq

Smart Contract Classification with a Bi-LSTM Based Approach

Gang Tian, Qibo Wang, Yi Zhao, Lantian Guo, Zhonglin Sun, Liangyu Lv
2020 IEEE Access  
Different from traditional text, the smart contract is composed of several parts: source code, code comments and other useful information like account information.  ...  We also use attention mechanism to focus on the more relevant features in smart contracts for tags and fuse account information to provide additional information for classification.  ...  ACKNOWLEDGMENT The authors would like to thank reviewers for the precious comments. (Gang Tian and Lantian Guo are co-first authors.)  ... 
doi:10.1109/access.2020.2977362 fatcat:lylgzbigs5bfpiksnay5e3ksve

Explainable User Clustering in Short Text Streams

Yukun Zhao, Shangsong Liang, Zhaochun Ren, Jun Ma, Emine Yilmaz, Maarten de Rijke
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
To address this problem, we propose a dynamic user clustering topic model (or UCT for short).  ...  Given the dynamic nature of social media, there is a need to dynamically cluster users in the context of short text streams.  ...  We thank Juan Echeverría Guzman at UCL for collecting the dataset for us. This work was supported by the National Natu-  ... 
doi:10.1145/2911451.2911522 dblp:conf/sigir/ZhaoLRMYR16 fatcat:7vfxmcyfuvgobikogqu7ywmtmu

Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization

Baohan Xu, Yanwei Fu, Yu-Gang Jiang, Boyang Li, Leonid Sigal
2018 IEEE Transactions on Affective Computing  
Specifically, our framework (1) learns a video encoding from an auxiliary emotional image dataset in order to improve supervised video emotion recognition, and (2) transfers knowledge from an auxiliary  ...  In this paper, for the first time, we study the problem of transferring knowledge from heterogeneous external sources, including image and textual data, to facilitate three related tasks in understanding  ...  ACKNOWLEDGMENTS The authors would like to thank Chong-Wah Ngo for his constructive advise.  ... 
doi:10.1109/taffc.2016.2622690 fatcat:huwcluofnnesfctwyknafpqd74

Leverage Label and Word Embedding for Semantic Sparse Web Service Discovery

Chengai Sun, Liangyu Lv, Gang Tian, Qibo Wang, Xiaoning Zhang, Lantian Guo
2020 Mathematical Problems in Engineering  
Information retrieval-based Web service discovery approach suffers from the semantic sparsity problem caused by lacking of statistical information when the Web services are described in short texts.  ...  Based on the topic model, the services are interpreted into hierarchical models for building a service querying and ranking model.  ...  Jin et al. utilized a transfer learning model to cluster short texts to embody auxiliary long texts [8] . ese approaches can partially handle the semantic sparsity problem; however, they also have some  ... 
doi:10.1155/2020/5670215 fatcat:ihzi5ugzz5cgbdy22w7tnvwbri

Mutual Clustering on Comparative Texts via Heterogeneous Information Networks [article]

Jianping Cao, Senzhang Wang, Danyan Wen, Zhaohui Peng, Philip S. Yu, Fei-yue Wang
2019 arXiv   pre-print
Next, two similarity matrices based on HINT as well as a transition matrix for cross-text-source knowledge transfer are constructed.  ...  To better organize the multi-sourced texts and obtain more comprehensive knowledge, we propose to study the novel problem of Mutual Clustering on Comparative Texts (MCCT), which aims to cluster the comparative  ...  The MCCT problem is also quite different from the works of clustering short texts Sahami et al, 2006) , which uses news or other text data as auxiliary information to facilitate short text clustering  ... 
arXiv:1903.03762v1 fatcat:mcl2rfbyhvf2bkqo33bhbmxxta
« Previous Showing results 1 — 15 out of 9,295 results