Wikipedia-based Semantic Interpretation for Natural Language Processing
This addresses the need for incorporating vast world knowledge into NLP systems, offering an explainable model that enhances performance in key tasks.
The paper tackles the problem of semantic interpretation in natural language processing by proposing Explicit Semantic Analysis (ESA), which uses Wikipedia concepts to represent meaning, resulting in significant improvements over previous state-of-the-art methods in text categorization and semantic relatedness tasks.
Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.