We further filtered out words based on Term Frequency-Inverse Document Frequency (TF-IDF), where words with high document frequencies and relatively insignificant for single document were removed.
Luhn developed a text summarization system which selects relevant sentences and generates text abstracts from biomedical literature based on term frequencies [ 1].
Such analysis is facilitated through use of text mining techniques, ranging from simple statistical pattern learning based on term frequencies, to complex natural language processing techniques in order to produce text categorization, document summarization, information retrieval, and ultimately the data mining [ 6].
Based on term document frequency matrix as input PLSA finds phishing and non-phishing topics using tempered expectation maximization.
This paper describes our approach to adapting a text document similarity classifier based on the Term Frequency Inverse Document Frequency (TFIDF) metric to two massively multi-core hardware platforms.
T F- I D F is based on the term frequency - inverse document frequency (t f- i d f), but instead of considering the occurrences of the terms in individual documents, T F- I D F uses contrasting corpora, in addition to the domain corpus, and it considers the term occurrences in each individual corpus.
The above indices represent written features which are based on noun term frequencies.
To obtain the semantic tags for the recommendation, we proposed a process in which, after preprocessing the Web resources, the folksonomy and the personomy terms, we extract a lightweight ontology from them and create a ranking based on the terms frequency and a mapping between the Web resource and folksonomy ontologies over the personomy ontology.
It can be seen from Table 1 that the context similarity-based methods outperform those methods based on the document frequency or term frequency.
Our function is based on the TF.IDF (Term Frequency x Inverse Document Frequency) metric of the Information Retrieval field ([Baeza-Yates and Ribeiro-Neto 2011]).
