scholarly journals A Methodology of Partner Selection for Sustainable Industry-University Cooperation Based on LDA Topic Model

2019 ◽  
Vol 11 (12) ◽  
pp. 3478 ◽  
Author(s):  
Jiho Kang ◽  
Junseok Lee ◽  
Dongsik Jang ◽  
Sangsung Park
Keyword(s):  
Strategic Alliances ◽  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Knowledge Based ◽  
University Cooperation ◽  
Selection For ◽  
The Right ◽  
Technology Domain ◽  
Specific Technology

In today’s knowledge-based society, industry-university cooperation (IUC) is recognized as an effective tool for technological innovation. Many studies have shown that selecting the right partner is essential to the success of the IUC. Although there have been a lot of studies on the criteria for selecting a suitable partner for IUC or strategic alliances, there has been a problem of making decisions depending on the qualitative judgment of experts or staff. While related works using patent analysis enabled the quantitative analysis and comparison of potential research partners, they overlooked the fact that there are several sub-technologies in one specific technology domain and that the applicant’s research concentration and competency are not the same for every sub-technology. This study suggests a systematic methodology that combines the Latent Dirichlet Allocation (LDA) topic model and the clustering algorithm in order to classify the sub-technology categories of a particular technology domain, and identifies the best college partners in each category. In addition, a similar-patent density (SPD) index was proposed and utilized for an objective comparison of potential university partners. In order to investigate the practical applicability of the proposed methodology, we conducted experiments using real patent data on the electric vehicle domain obtained from the Korean Intellectual Property Office. As a result, we identified 10 research and development sectors wherein Hyundai Motor Company (HMC) focuses using LDA and clustering. The universities with the highest values of SPD for each sector were chosen to be the most suitable partners of HMC for collaborative research.

Incorporating Biterm Correlation Knowledge into Topic Modeling for Short Texts

2020 ◽  
Author(s):  
Kai Zhang ◽  
Yuan Zhou ◽  
Zheng Chen ◽  
Yufei Liu ◽  
Zhuo Tang ◽  
...  
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Semantic Knowledge ◽  
Superior Performance ◽  
Knowledge Based ◽  
Modeling Process ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Latent Topic

Abstract The prevalence of short texts on the Web has made mining the latent topic structures of short texts a critical and fundamental task for many applications. However, due to the lack of word co-occurrence information induced by the content sparsity of short texts, it is challenging for traditional topic models like latent Dirichlet allocation (LDA) to extract coherent topic structures on short texts. Incorporating external semantic knowledge into the topic modeling process is an effective strategy to improve the coherence of inferred topics. In this paper, we develop a novel topic model—called biterm correlation knowledge-based topic model (BCK-TM)—to infer latent topics from short texts. Specifically, the proposed model mines biterm correlation knowledge automatically based on recent progress in word embedding, which can represent semantic information of words in a continuous vector space. To incorporate external knowledge, a knowledge incorporation mechanism is designed over the latent topic layer to regularize the topic assignment of each biterm during the topic sampling process. Experimental results on three public benchmark datasets illustrate the superior performance of the proposed approach over several state-of-the-art baseline models.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

scholarly journals Intelligent radar software defect classification approach based on the latent Dirichlet allocation topic model

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xi Liu ◽  
Yongfeng Yin ◽  
Haifeng Li ◽  
Jiabin Chen ◽  
Chang Liu ◽  
...  
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Recall Rate ◽  
Defect Classification ◽  
Software Defects ◽  
Classification Approach ◽  
Software Defect ◽  
Model Combining ◽  
Dirichlet Allocation

AbstractExisting software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 ~ 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.

scholarly journals Technology Hotspot Tracking: Topic Discovery and Evolution of China’s Blockchain Patents Based on a Dynamic LDA Model

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 415
Author(s):  
Jinli Wang ◽  
Yong Fan ◽  
Hui Zhang ◽  
Libo Feng
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Graph Model ◽  
Representation Learning ◽  
Research Direction ◽  
Calculation Model ◽  
Topic Evolution ◽  
Blockchain Technology ◽  
The Status ◽  
Research Hotspots

Tracking scientific and technological (S&T) research hotspots can help scholars to grasp the status of current research and develop regular patterns in the field over time. It contributes to the generation of new ideas and plays an important role in promoting the writing of scientific research projects and scientific papers. Patents are important S&T resources, which can reflect the development status of the field. In this paper, we use topic modeling, topic intensity, and evolutionary computing models to discover research hotspots and development trends in the field of blockchain patents. First, we propose a time-based dynamic latent Dirichlet allocation (TDLDA) modeling method based on a probabilistic graph model and knowledge representation learning for patent text mining. Second, we present a computational model, topic intensity (TI), that expresses the topic strength and evolution. Finally, the point-wise mutual information (PMI) value is used to evaluate topic quality. We obtain 20 hot topics through TDLDA experiments and rank them according to the strength calculation model. The topic evolution model is used to analyze the topic evolution trend from the perspectives of rising, falling, and stable. From the experiments we found that 8 topics showed an upward trend, 6 topics showed a downward trend, and 6 topics became stable or fluctuated. Compared with the baseline method, TDLDA can have the best effect when K is 40 or less. TDLDA is an effective topic model that can extract hot topics and evolution trends of blockchain patent texts, which helps researchers to more accurately grasp the research direction and improves the quality of project application and paper writing in the blockchain technology domain.

Strategic alliances of shopping centers in Brazil: network perspective

2017 ◽  
Vol 15 (2) ◽  
pp. 167-186 ◽  
Author(s):  
Renata Maria de Almeida Bastos Gomes ◽  
Fabio de Oliveira Paula ◽  
T. Diana L. van Aduard de Macedo-Soares
Keyword(s):  
Strategic Alliances ◽  
Content Type ◽  
Network Characteristics ◽  
Alliance Networks ◽  
Slowing Down ◽  
Contractual Arrangements ◽  
Management Perspective ◽  
Relational Factors ◽  
The Right

Purpose The shopping center (SC) industry in emerging countries has grown fast over the past decade; however, recently, it is showing signs of slowing down. Nevertheless, some SC-companies perform well. As those firms operate in alliance networks, relational opportunities and risks should be considered in their strategic analyses. Although there is a significant amount of research on SC from a marketing perspective, there is a dearth of research on strategic alliances from an SC management perspective. This paper aims at answering the following question: How do characteristics of the alliance networks of leading SC-companies contribute to their success by mitigating the structural threats the SC-industry in Brazil is facing? Design The case study method was adopted for analyzing two leading Brazilian SC-companies. Several data sources were used to allow for data triangulation. The lack of literature on strategic alliances and the SC-industry, as well as the research’s exploratory nature, justified this choice. Findings The research made evident that the SC-companies’ alliance network characteristics not only mitigate some of the structural industry threats but also enhance opportunities. It illustrated how firms can conduct a strategic analysis from a network perspective with the right tools. It also made evident how much more accurate the results of a comprehensive relational analysis are compared with traditional analyses that do not consider the strategic implications of relational factors. Practical implications The research contributed to SC management by highlighting the importance of taking into account the network characteristics of their relationships with key partners and of considering these as alliances and not merely contractual arrangements. Originality/value There is a dearth of research on the strategic implications of alliances of firms that own and manage a portfolio of SCs, as well as of their relationships with other actors in the industry, such as retailers and real estate owners, from a network perspective.

Towards a knowledge-based probabilistic and context-aware social recommender system

2017 ◽  
Vol 44 (4) ◽  
pp. 464-490 ◽  
Author(s):  
Luis Omar Colombo-Mendoza ◽  
Rafael Valencia-García ◽  
Alejandro Rodríguez-González ◽  
Ricardo Colomo-Palacios ◽  
Giner Alor-Hernández
Keyword(s):  
Latent Dirichlet Allocation ◽  
Evaluation Method ◽  
Context Aware ◽  
Average Precision ◽  
Case Scenario ◽  
Similarity Metric ◽  
Knowledge Based ◽  
Context Modelling ◽  
Evaluation Metric ◽  
Partial Implementation

In this article, we propose (1) a knowledge-based probabilistic collaborative filtering (CF) recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation (LDA) model-based recommendation technique and (2) a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain (foodservice places). The ontology on which the similarity metric is based is additionally leveraged to model and reason about users’ contexts; the proposed LDA model also guides the users’ context modelling to some extent. An evaluation method in the form of a comparative analysis based on traditional information retrieval (IR) metrics and a reference ranking-based evaluation metric (correctly ranked places) is presented towards the end of this article to reliably assess the efficacy and effectiveness of our recommendation approach, along with its utility from the user’s perspective. Our recommendation approach achieves higher average precision and recall values (8% and 7.40%, respectively) in the best-case scenario when compared with a CF approach that employs a baseline similarity metric. In addition, when compared with a partial implementation that does not consider users’ preferences for topics, the comprehensive implementation of our recommendation approach achieves higher average values of correctly ranked places (2.5 of 5 versus 1.5 of 5).

The value, concept and essence of appeal proceedings in civil cases

2020 ◽  
pp. 523-529
Author(s):  
Liliya Usich
Keyword(s):  
Human Rights ◽  
Knowledge Based ◽  
Civil Proceedings ◽  
High Prevalence ◽  
Domestic Legislation ◽  
The Russian Federation ◽  
Civil Cases ◽  
Judicial Errors ◽  
The Right ◽  
Judicial Protection

This work is devoted to identifying the significance of the appeal proceedings in civil cases. We emphasize that the right to judicial protection is one of the fundamental human rights. To achieve this goal, we set the following tasks: define the concept of appeal proceedings; characterize the essence of the appeal proceedings in civil cases. In the course of studying the issue, we use the methods of scientific knowledge, based on the results of which the appropriate conclusions are drawn: despite the wide recognition of the appeal proceedings in the Russian Federation, we note the need to improve the efficiency of this institution due to certain omissions in the legislation. As a result, we define what should be understood as an appeal – consideration of cases that have not entered into legal force. By virtue of this, the importance and significance of the appeal proceedings as an appeal tool, as well as the direct correction of judicial errors, is noted both by the norms of domestic legislation and by international human rights bodies. The indicated gaps in the legislation show the absence of clearly defined boundaries, which creates problems in determining the value and essence of the appeal proceedings both at the theoretical and practical levels. In particular, there is a controversy on the appeal proceedings’ importance. However, the doctrine identifies two main elements, the essence of the appeal proceedings is: 1) the repetition of the case; 2) verification of the judicial act. Nevertheless, despite the high prevalence of appeals in civil proceedings, the issue of improving the effectiveness of this institution is still relevant, which leads to the inefficiency of civil proceedings in general.

Sign in / Sign up

Export Citation Format

Share Document