A local context‐aware LDA model for topic modeling in a document network

Liu, Yang; Li, Zongfang

doi:10.1002/asi.23822

Cited by 10 publications

(6 citation statements)

References 42 publications

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“…Network clustering is an unsupervised approach to clustering network objects with affinity [4,24,25,26,27]; document networks or social networks are often clustered to analyze datasets without expending much human effort. Network-based clustering is different from contentbased clustering in that it clusters documents based on connectivity.…”

Section: Network-based Clusteringmentioning

confidence: 99%

Gated Multi-channel Network Embedding for Large-scale Mobile App Clustering

2023

KSII TIIS

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This paper studies the task of embedding nodes with multiple graphs representing multiple information channels, which is useful in a large volume of network clustering tasks. By learning a node using multiple graphs, various characteristics of the node can be represented and embedded stably. Existing studies using multi-channel networks have been conducted by integrating heterogeneous graphs or limiting common nodes appearing in multiple graphs to have similar embeddings. Although these methods effectively represent nodes, it also has limitations by assuming that all networks provide the same amount of information. This paper proposes a method to overcome these limitations; The proposed method gives different weights according to the source graph when embedding nodes; the characteristics of the graph with more important information can be reflected more in the node. To this end, a novel method incorporating a multi-channel gate layer is proposed to weigh more important channels and ignore unnecessary data to embed a node with multiple graphs. Empirical experiments demonstrate the effectiveness of the proposed multi-channel-based embedding methods.

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Section: Network-based Clusteringmentioning

confidence: 99%

Gated Multi-channel Network Embedding for Large-scale Mobile App Clustering

2023

KSII TIIS

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“…It is used to calculate the distances between documents and subsequently to cluster similar documents during documentclustering tasks. Some widely used document-embedding methods include those that use the term frequency (TF) or the inverse document frequency (IDF) [22], as well as the topic-modeling-based method called latent Dirichlet allocation (LDA) [6], [14], [19], [23]. Examples of studies in which these methods have been used for document clustering include one in which document clustering was performed by applying topic-modeling-based document embedding to the k-means algorithm [6].…”

Section: B Document-embedding-based Document Clusteringmentioning

confidence: 99%

“…Examples of studies in which these methods have been used for document clustering include one in which document clustering was performed by applying topic-modeling-based document embedding to the k-means algorithm [6]. In another study [14], the performance of topic modeling was improved by measuring the significance of documents based on their network; the improved method was then used for document clustering. In addition, research is currently underway on embedding documents or words using neural networks.…”

Section: B Document-embedding-based Document Clusteringmentioning

confidence: 99%

“…The probability distribution is calculated by adding and normalizing the edge weights of all the neighbors, as shown in (13). In this study, the weight calculation method used in a previous study [27] was modified for mobile apps and used to measure the weight based on the TF and the developer information shared between two documents, as shown in (14).…”

Section: B Clustering Performancementioning

confidence: 99%

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Network-Based Document Clustering Using External Ranking Loss for Network Embedding

Yoon

Gee

Lim³

2019

IEEE Access

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Network-based document clustering involves forming clusters of documents based on their significance and relationship strength. This approach can be used with various types of metadata that express the significance of the documents and the relationships among them. In this study, we defined a probabilistic network graph for fine-grained document clustering and developed a probabilistic generative model and calculation method. Furthermore, a novel neural-network-based network embedding learning method was devised that considers the significance of a document based on its rankings with external measures, such as the download counts of relevant files, and reflects the relationship strength between the documents. By considering the significance of a document, reputative documents of clusters can be centralized and shown as representative documents for tasks such as data analysis and data representation. During evaluation tests, the proposed ranking-based network-embedding method performs significantly better on various algorithms, such as the k-means algorithm and common word/phrase-based clustering methods, than the existing network embedding approaches.INDEX TERMS Clustering algorithms, artificial neural networks.

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“…Song et al proposed a proximity probabilistic model (PPM), which uses a position‐dependent term count to represent both the number of occurrences of a term and the term counts propagated from other terms. Quite recently, topic models have been widely used to explore latent term associations in knowledge discovery and other related areas . Liu and Croft in particular proposed cluster‐based retrieval models under the language modeling framework, which has been used to smooth the probabilities in the document model.…”

Section: Related Workmentioning

confidence: 99%

A topic‐based term frequency normalization framework to enhance probabilistic information retrieval

Jian

Huang

Zhao

et al. 2019

Computational Intelligence

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Many well‐known probabilistic information retrieval models have shown promise for use in document ranking, especially BM25. Nevertheless, it is observed that the control parameters in BM25 usually need to be adjusted to achieve improved performance on different data sets; additionally, the assumption in BM25 on the bag‐of‐words model prevents its direct utilization of rich information that lies at the sentence or document level. Inspired by the above challenges with respect to BM25, we first propose a new normalization method on the term frequency in BM25 (called BM25QL in this paper); in addition, the method is incorporated into CRTER2, a recent BM25‐based model, to construct CRTER2QL. Then, we incorporate topic modeling and word embedding into BM25 to relax the assumption of the bag‐of‐words model. In this direction, we propose a topic‐based retrieval model, TopTF, for BM25, which is then further incorporated into the language model (LM) and the multiple aspect term frequency (MATF) model. Furthermore, an enhanced topic‐based term frequency normalization framework, ETopTF, based on embedding is presented. Experimental studies demonstrate the great effectiveness and performance of these methods. Specifically, on all tested data sets and in terms of the mean average precision (MAP), our proposed models, BM25QL and CRTER2QL, are comparable to BM25 and CRTER2 with the best b parameter value; the TopTF models significantly outperform the baselines, and the ETopTF models could further improve the TopTF in terms of the MAP.

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A local context‐aware LDA model for topic modeling in a document network

Cited by 10 publications

References 42 publications

Gated Multi-channel Network Embedding for Large-scale Mobile App Clustering

Gated Multi-channel Network Embedding for Large-scale Mobile App Clustering

Network-Based Document Clustering Using External Ranking Loss for Network Embedding

A topic‐based term frequency normalization framework to enhance probabilistic information retrieval

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