TM-SGTD: Text Mining Based Semantic Graph for Text Document Approach for Text Representation

A Semantic Graph Model for Text Representation and Matching in Document Mining

A semantic partition based text mining model for document classification

Feature Extraction is a mechanism used to extract key phrases from any given text documents. This extraction can be weighted, ranked or semantic based. Weighted and Ranking based feature extraction normally assigns scores to extracted words based on various heuristics. Highest scoring words are seen as important. Semantic based extractions normally try to understand word meanings, and words wit...

A Joint Semantic Vector Representation Model for Text Clustering and Classification

Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...

Corpus based coreference resolution for Farsi text

"Coreference resolution" or "finding all expressions that refer to the same entity" in a text, is one of the important requirements in natural language processing. Two words are coreference when both refer to a single entity in the text or the real world. So the main task of coreference resolution systems is to identify terms that refer to a unique entity. A coreference resolution tool could be...

KNN based Machine Learning Approach for Text and Document Mining

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learni...