Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge

Abstract

Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category. In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation. ECKA is a multi-level short text semantic representation model, which extracts the semantic features from the word, entity, concept and knowledge levels by CNN, respectively. Since word, entity, concept and knowledge entity in the same short text have different cognitive informativeness for short text classification, attention networks are formed to capture these category-related attentive representations from the multi-level textual features, respectively. The final multi-level semantic representations are formed by concatenating all of these individual-level representations, which are used for text classification. Experiments on three tasks demonstrate our method significantly outperforms the state-of-the-art methods.

Text2shape Deep Retrieval Model: Generating Initial Cases for Mechanical Part Redesign under the Context of Case-Based Reasoning

“…The model can generate accurate word vector representations, and it can also identify polysemous words in the corpus through a context clustering algorithm. Hou et al [32] proposed an Entity-based Concept Knowledge-Aware (ECKA) model, which can integrate semantic information into short text representations. The model was developed based on the CNN algorithm, and it can extract semantic features from words, entities, concepts, and knowledge layers.…”

Cognitive Computing with a Big Data System in a Secure Internet of Things

“…Short text is widely seen in applications including Internet of Things. The Entitybased Concept Knowledge-Aware (ECKA) method is a multi-level short text semantic representation model that extracts semantic features from words, entities, and concepts with different knowledge levels, as explained in article [9].…”