who do not always agree when confronted with the task of selecting a sense from a dictionary that matches a word in a context. The granularity issue has been tackled by proposing clustering methods that automatically group together similar senses of the same word. Offensive content WordNet includes words that can be perceived as pejorative or offensive. The interpretation of a word can change over time and between social groups, so it is not always possible for WordNet to define a word as "pejorative" or "offensive" in isolation. Therefore, people using WordNet must apply their own methods to identify offensive or pejorative words. However, this limitation is true of other lexical resources like dictionaries and thesauruses, which also contain pejorative and offensive words. Some dictionaries indicate words that are pejoratives, but do not include all the contexts in which words might be acceptable or offensive to different social groups. Therefore, people using dictionaries must apply their own methods to identify all offensive words. Licensed vs. Open WordNets Some wordnets were subsequently created for other languages. A 2012 survey lists the wordnets and their availability. In an effort to propagate the usage of WordNets, the Global WordNet community had been slowly re-licensing their WordNets to an open domain where researchers and developers can easily access and use WordNets as language resources to provide ontological and lexical knowledge in natural-language processing (NLP) tasks. The Open Multilingual WordNet provides access to open licensed wordnets in a variety of languages, all linked to the Princeton Wordnet of English (PWN). The goal is to make it easy to use wordnets in multiple languages. Applications WordNet has been used for a number of purposes in information systems, including word-sense disambiguation, information retrieval, automatic text classification, automatic text summarization, machine translation and even automatic crossword puzzle generation. A common use of WordNet is to determine the similarity between words. Various algorithms have been proposed, including measuring the distance among words and synsets in WordNet's graph structure, such as by counting the number of edges among synsets. The intuition is that the closer two words or synsets are, the closer their meaning. A number of WordNet-based word similarity algorithms are implemented in a Perl package called WordNet::Similarity, and in a Python package called NLTK. Other more sophisticated WordNet-based similarity techniques include ADW, whose implementation is available in Java. WordNet can also be used to inter-link other vocabularies. Interfaces