Accurate semantic modeling lies at the very core of today’s Natural Language Processing (NLP). Getting a handle on the various phenomena that regulate the meaning of linguistic utterances can pave the way for solving many compelling and ambitious tasks in the field, from Machine Translation to Question Answering and Information Retrieval. A complete semantic model of language, however, needs first of all reliable building blocks. In the last two decades, research in lexical semantics (which focuses on the meaning of individual linguistic elements, i.e., words and expressions), has produced increasingly comprehensive and effective machine-readable dictionaries in multiple languages: like humans, NLP systems can now leverage these sources of lexical knowledge to discriminate among various senses of a given lexeme, thereby improving their performances on downstream tasks and applications. In this paper, we focus on the case study of BabelNet, a large multilingual encyclopedic dictionary and semantic network, to describe in detail how such knowledge resources are built, improved and exploited for crucial NLP tasks such as Word Sense Disambiguation, Entity Linking and Semantic Similarity.
Semantic Network Array Processor as a massively parallel computing platform for high performance and large-scale natural language processing
