KeypartX: Graph-based Perception (Text) Representation
This addresses the challenge of transforming unstructured big data into meaningful representations for individuals, businesses, and academics, though it appears incremental as it builds on existing text representation techniques.
The study tackled the problem of representing unstructured text data by developing KeypartX, a graph-based approach that uses key parts of speech to extract meaning from linguistic information, resulting in a method applicable even to single sentences without requiring massive data.
The availability of big data has opened up big opportunities for individuals, businesses and academics to view big into what is happening in their world. Previous works of text representation mostly focused on informativeness from massive words' frequency or cooccurrence. However, big data is a double-edged sword which is big in volume but unstructured in format. The unstructured edge requires specific techniques to transform 'big' into meaningful instead of informative alone. This study presents KeypartX, a graph-based approach to represent perception (text in general) by key parts of speech. Different from bag-of-words/vector-based machine learning, this technique is human-like learning that could extracts meanings from linguistic (semantic, syntactic and pragmatic) information. Moreover, KeypartX is big-data capable but not hungry, which is even applicable to the minimum unit of text:sentence.