The topic words and the entity words together qualify in determining topic or sub-topic association of documents or news reports.
In this study, we adopt the concepts of topic of document and word link to disambiguate biomedical abbreviations.
Machine learning algorithm such as reinforcement learning has been utilized in the task of Text Mining [108] applications for learning aspect or topic of document or story [109] as tasks in computing long term rewards and thus making these methods applicable to Big Data Analytics [71].
The authors proposed an early adoption based information flow (EABIF) network by comparing the timestamps when users access documents, and a topic-sensitive early adoption based information propagation (TEABIF) network according to the topics of the documents users accessed [27].
"The general topics of the documents would provide the defendant exactly the information he seeks, listings of the pressing matters presented to him during the times relevant to the case".
Therefore, the comparison between two sequencing data is processed as "topic analysis of documents" in text mining.
To cover more topics of a document, we perform several clustering algorithms based on semantics metrics in Sect. 4.
Then, we apply clustering algorithms on keyterms based on semantic similarity which aims at covering more topics of the document.
The existing method SC [17] also proposes a clustering approach to cover the topics of a document.
[Keyphrase] We define keyphrase as a continuous sequence of keyterms, which is highly important and relevant to the topics of a document.
