A phrase is a natural, meaningful, and essential semantic unit. In topic modeling, visualizing phrases for individual topics is an effective way to explore and understand unstructured text corpora. However, from phrase quality and topical cohesion perspectives, the outcomes of existing approaches remain to be improved. Usually, the process of topical phrase mining is twofold: phrase mining and topic modeling. For phrase mining, existing approaches often suffer from order sensitive and inappropriate segmentation problems, which make them often extract inferior quality phrases. For topic modeling, traditional topic models do not fully consider the constraints induced by phrases, which may weaken the cohesion. Moreover, existing approaches oft...
Short texts are a common source of knowledge, and the extraction of such valuable information is ben...
There are many popular models available for classification of documents like Naïve Bayes Classifier...
The goal of topic detection or topic modelling is to uncover the hidden topics in a large corpus. It...
A phrase is a natural, meaningful, essential semantic unit. In topic modeling, visualizing phrases f...
While most topic modeling algorithms model text corpora with unigrams, human interpretation often re...
A sentence is an integral unit of semantic nature, context and significance. Visualizing sentences f...
One of the major challenges of mining topics from a large corpus is the quality of the constructed t...
Phrase mining is a key research problem for semantic analysis and text-based information retrieval. ...
Phrase snippets of large text corpora like news articles or web search results offer great insight a...
Most topic models, such as latent Dirichlet allocation, rely on the bag of words assumption. However...
In clinical documents, medical terms are often expressed in multi-word phrases. Traditional topic mo...
We propose a method for supporting query refinement using topical term clusters. First, we propose a...
Keyword searching is the most common form of document search on the Web. Many Web publishers manuall...
Making sense of words often requires to simultaneously examine the surrounding context of a term as ...
Text data are ubiquitous and play an essential role in big data applications. However, text data are...
Short texts are a common source of knowledge, and the extraction of such valuable information is ben...
There are many popular models available for classification of documents like Naïve Bayes Classifier...
The goal of topic detection or topic modelling is to uncover the hidden topics in a large corpus. It...
A phrase is a natural, meaningful, essential semantic unit. In topic modeling, visualizing phrases f...
While most topic modeling algorithms model text corpora with unigrams, human interpretation often re...
A sentence is an integral unit of semantic nature, context and significance. Visualizing sentences f...
One of the major challenges of mining topics from a large corpus is the quality of the constructed t...
Phrase mining is a key research problem for semantic analysis and text-based information retrieval. ...
Phrase snippets of large text corpora like news articles or web search results offer great insight a...
Most topic models, such as latent Dirichlet allocation, rely on the bag of words assumption. However...
In clinical documents, medical terms are often expressed in multi-word phrases. Traditional topic mo...
We propose a method for supporting query refinement using topical term clusters. First, we propose a...
Keyword searching is the most common form of document search on the Web. Many Web publishers manuall...
Making sense of words often requires to simultaneously examine the surrounding context of a term as ...
Text data are ubiquitous and play an essential role in big data applications. However, text data are...
Short texts are a common source of knowledge, and the extraction of such valuable information is ben...
There are many popular models available for classification of documents like Naïve Bayes Classifier...
The goal of topic detection or topic modelling is to uncover the hidden topics in a large corpus. It...