Paragraphs and texts, which are made up of sequences of sentences. Sentences, that is, grammatical sequences of words which typically include a main clause, made up of a predicate, a subject and, possibly, other syntactic elements. Word senses, that is, the meanings that words convey in given contexts (e.g., the device meaning vs. Words, which are the basic building blocks of language, also including their inflectional information. The second aspect concerns the type of linguistic item to be analysed, which can be: Furthermore, hybrid semantic similarity combines both knowledge-based and distributional methods. Accordingly, we distinguish between knowledge-based semantic similarity, in the former case, and distributional semantic similarity, in the latter. The first concerns the type of resource employed, whether it be a lexical knowledge base (LKB), that is, a wide-coverage structured repository of linguistic data, or large collections of raw textual data, that is, corpora. In general, semantic similarity can be classified on the basis of two fundamental aspects. An explicative illustration of word similarity and relatedness. In this paper, relatedness will not be discussed and the focus will lie on similarity.įigure 1. In fact, similarity is often considered to be a specific instance of relatedness (Jurafsky Reference Jurafsky2000), where the concepts evoked by the two words belong to the same ontological class. As is apparent from Figure 1, beautiful and appeal are related but not similar, whereas pretty and cute are both related and similar. Relatedness encompasses a much larger set of semantic relations, ranging from antonymy ( beautiful and ugly) to correlation ( beautiful and appeal). In fact, while similarity refers to items which can be substituted in a given context (such as cute and pretty)without changing the underlying semantics, relatedness indicates items which have semantic correlations but are not substitutable. However, before examining such approaches, it is crucial to provide a definition of similarity: what is meant exactly by the term ‘similar’? Are all semantically related items ‘similar’? Resnik ( Reference Resnik1995) and Budanitsky and Hirst ( Reference Budanitsky and Hirst2001) make a fundamental distinction between two apparently interchangeable concepts, that is, similarity and relatedness. Over the last two decades, several different approaches have been put forward for computing similarity using a variety of methods and techniques. taxonomical information: generalizations (i.e., more general concepts) and specializations (i.e.Measuring the degree of semantic similarity between linguistic items has been a great challenge in the field of Natural Language Processing (NLP), a sub-field of Artificial Intelligence concerned with the handling of human language by computers.definitions (often multiple) in each language.What makes WordAtlas special is its linkage between concepts and words in hundreds of languages: WordAtlas provides millions of lexicalizations for each language, from common nouns, adjectives, verbs and adverbs, to hundreds of thousands of technical terms and millions of named entities, such as people, locations, organizations and products.Įach meaning comes with a wealth of information, including: Roberto Navigli’s lab at the Sapienza University of Rome. It greatly enhances BabelNet®, the award-winning multilingual semantic network, thanks to the know-how of years of research in computational linguistics in Prof. WordAtlas is the next-generation multilingual knowledge graph. They organize knowledge into a coherent network of meanings and they enable Artificial Intelligence applications which exploit this knowledge to perform text understanding. Knowledge graphs are the 21st century counterpart of dictionaries in previous centuries.
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