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TRM – Learning Dependencies between Text and Structure with Topical Relational Models
[chapter]
2013
Lecture Notes in Computer Science
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We propose an approach, called Topical Relational Model, as a principled approach for automatically learning topics from both textual and structure information. ...
Text-rich structured data become more and more ubiquitous on the Web and on the enterprise databases by encoding heterogeneous structural relationships between entities such as people, locations, or organizations ...
In this paper, we propose Topical Relational Model (TRM) that uses relational information in structured data for topic modeling (text analysis tasks), and also allows the learned topics to be employed ...
doi:10.1007/978-3-642-41335-3_1
fatcat:jkqusnnyjzhspbfazew7jijvki
Semi-automatic Technology Roadmapping Composing Method for Multiple Science, Technology, and Innovation Data Incorporation
[chapter]
2016
Innovation, Technology, and Knowledge Management
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TRM composing model with expert aid. ...
Addressing these concerns, this paper proposes a TRM composing method with a clustering-based topic identification model, a multiple science data sources integration model, and a semi-automated fuzzy set-based ...
, we re-arrange the hieratical structure of TRM composing model in [19] and enrich the relations between components on the multi-layer landscape to display detailed topic changing routes for assessment ...
doi:10.1007/978-3-319-39056-7_12
fatcat:2zfhh4ju4fb65hchzgb6etuehu
BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer
[article]
2020
arXiv
pre-print
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In our proposed system, the modalities are derived from text (articles published by candidates) and graph (their co-author connections) information. ...
BERTERS converts text into a vector using the Bidirectional Encoder Representations from Transformer (BERT). ...
Moreover, authors introduced a topic-sensitive method to reflect both the link structure and the topic relevance between questioners and answerers. ...
arXiv:2007.07229v1
fatcat:pknabu6xqrfqndyp6dsmkd2gye
Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research
2016
Technological forecasting & social change
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By addressing and predicting these changes, this paper proposes an analytic method to (1) cluster associated terms and phrases to constitute meaningful technological topics and their interactions, and ...
(2) identify changing topical emphases. ...
"Related Works" reviews previous studies including text clustering, topic analysis, TRM, and a comparison between our research and related works. ...
doi:10.1016/j.techfore.2016.01.015
fatcat:zjqqq2pwondftgvl6zjbpq6c2y
Hierarchical Network Emotional Assistance Mechanism for Emotion Cause Extraction
2022
Security and Communication Networks
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, and structural information between clause neighborhoods. ...
Most of the existing work regards the task as an independent text clause classification problem, ignoring the relationship between the clauses and failing to use the indicative relationship between emotional ...
It proves that the HNEAM model reduces the information loss caused by the long-distance dependence of clauses due to the learning of the semantic and structural information of the text and can effectively ...
doi:10.1155/2022/3597771
fatcat:rq4emposbve2lfpro325yva2za
Holistic and Compact Selectivity Estimation for Hybrid Queries over RDF Graphs
[chapter]
2014
Lecture Notes in Computer Science
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This way, we capture correlations between structured and unstructured data in a holistic and compact manner. ...
We propose a novel estimation approach, TopGuess, which exploits topic models as data synopsis. ...
These topic models summarize the data by means of one uniform synopsis -considering structured and text data. ...
doi:10.1007/978-3-319-11915-1_7
fatcat:ufz44u3z4zdedooeusolv2qthi
Developing a risk-adaptive technology roadmap using a Bayesian network and topic modeling under deep uncertainty
2021
Scientometrics
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First we identify risk by topic modeling based on futuristic data and then by sentiment analysis. ...
Third, we convert an existing TRM to network topology with adaptive plans and construct a conditional probability table for the network. ...
We used a Bayesian network and text mining including topic modeling to develop the risk-adaptive roadmap because Bayesian networks allow for considering relationships between nodes and make it possible ...
doi:10.1007/s11192-021-03945-8
pmid:33776164
pmcid:PMC7980740
fatcat:tcymybya5nd2dgl6uhyj3u3i6e
Development of Morphology Analysis-Based Technology Roadmap Considering Layer Expansion Paths: Application of TRIZ and Text Mining
2020
Applied Sciences
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solving (TRIZ) inventive principles to establish innovation paths for new opportunities with the aid of text mining tools. ...
Thus, the aim of this research is to develop MA-based TRMs by utilizing MA to describe the characteristics of the technology and product layers in the TRMs and apply the improved theory of inventive problem ...
The models pulled by market and pushed by technology have interconnections with the market and technology layers in TRMs, which could be used to explain the integration between MA and TRMs. Yoon et al ...
doi:10.3390/app10238498
fatcat:gbpho32oyrfdbfrlis55rcuefy
SENTIMENT ANALYSIS FOR SOCIAL MEDIA: A SURVEY
2021
Journal of Computer Science and Cybernetics
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In addition, discussions and comparisons related to these methods are provided. Additionally, we discuss the challenges and possible research directions for future research in this field. ...
With the rapid development of the Internet industry, an increasing number of social media platforms have been developed. ...
Therefore, before using machine-learning and deep-learning algorithms, it is essential to convert text data into numerical vectors by building text representation models (TRMs). ...
doi:10.15625/1813-9663/37/4/15892
fatcat:2dgv3sygovgelk3mffmvnmokay
Technology Roadmapping of Emerging Technologies: Scientometrics and Time Series Approach
[chapter]
2018
Scientometrics
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These methods are scientometrics, with which a customized and clean database is generated; hierarchical clustering to generate the ontology of the technology; principal component analysis, which is used ...
The present work is framed within tech mining and technology forecasting fields. ...
fuzzy set-based TRM composing model with expert aid. ...
doi:10.5772/intechopen.76675
fatcat:6cycw6vqj5banblmud2bgmuwwi
Automatic Generation of the Draft Procuratorial Suggestions Based on an Extractive Summarization Method: BERTSLCA
2021
Mathematical Problems in Engineering
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kernels with different sizes are designed to extract the relationships between adjacent sentences. ...
of the text and in turn leads to an adverse summarization performance. ...
Our model can better extract long dependency and relations between sentences.
Training Loss Convergence. ...
doi:10.1155/2021/3591894
fatcat:vhj5yxhv6jeapbadyaxg3mdnt4
Sentiment Analysis of online reviews based on LDA and AP-Bert model
2022
Highlights in Science, Engineering and Technology
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Firstly, LDA topic extraction model is used to extract the topic of online review text, and the concerned attributes are extracted. ...
According to the characteristics of online comments, a BERT emotion analysis model with enhanced pooling was proposed. ...
However, the deep learning model is not suitable for review texts with such features as multi-dependent word vector encoding or one-HOT encoding in the text embedding layer. ...
doi:10.54097/hset.v1i.472
fatcat:dts27tiz7fbbjjte2ptds2vdw4
A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods
2021
Mathematics
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Latent Dirichlet allocation (LDA) will be adopted to derive topic models based on the data retrieved from social media. ...
., the mutual influences between altruistic concerns and egoistic concerns, as well as those between altruistic concerns and biosphere concerns, are worth further investigation in future. ...
A corpus D is defined as a collection
Text Mining, Topic Model and LDA Text mining was first proposed by Fledman et al. [28] . ...
doi:10.3390/math9172041
fatcat:zewlvwxazzhwtgymch3z4qpy4i
A literature review on the state-of-the-art in patent analysis
2014
World Patent Information
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patents quality and the most promising patents, and identifying technological hotspots and patent vacuums. ...
Moreover, the key features and weaknesses of the discussed tools and techniques are presented and several directions for future research are highlighted. ...
Khan's research interests include optimization, robustness, and security of: cloud, grid, cluster and big data computing, social networks, wired and wireless networks, power systems, smart grids, and optical ...
doi:10.1016/j.wpi.2013.12.006
fatcat:prs6spbuhfd6lheiocbw5jz3di
DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation
[article]
2020
arXiv
pre-print
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For intra-modal, we construct a novel Hierarchical Transformer that can easily switch between sequential and feed-forward structures according to the differentiated context preference within each modality ...
For inter-modal, we constitute a novel Multi-Grained Interactive Fusion that applies both neuron- and vector-grained feature interactions to learn the differentiated contributions across all modalities ...
The "67.67" result in the text-only column is based on our intra-modal module fed with text-only features. Comparison with SOTA. DialogueRNN is the SOTA model for ERC in multi-modal settings. ...
arXiv:2010.07637v1
fatcat:dgihpizrdfenrgayok7mketuj4
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