WordNet

Last updated
WordNet
Developer(s) Princeton University
Initial releasemid 1980s
Stable release
3.1 / June 2011;13 years ago (2011-06) [1]
Written in Prolog
Operating system Unix, Linux, Solaris, Windows
Size 16MB (including 155,327 words organized in 175,979 synsets for a total of 207,016 word-sense pairs)
Available inMore than 200 languages
Type Lexical database
Licence BSD-like
Website wordnet.princeton.edu

WordNet is a lexical database of semantic relations between words that links words into semantic relations including synonyms, hyponyms, and meronyms. The synonyms are grouped into synsets with short definitions and usage examples. It can thus be seen as a combination and extension of a dictionary and thesaurus. While it is accessible to human users via a web browser, [2] its primary use is in automatic text analysis and artificial intelligence applications. It was first created in the English language [3] and the English WordNet database and software tools have been released under a BSD style license and are freely available for download from that WordNet website. There are now WordNets in more than 200 languages. [4]

Contents

History and team members

WordNet was first created in 1985, in English only, in the Cognitive Science Laboratory of Princeton University under the direction of psychology professor George Armitage Miller. It was later directed by Christiane Fellbaum. The project was initially funded by the U.S. Office of Naval Research, and later also by other U.S. government agencies including the DARPA, the National Science Foundation, the Disruptive Technology Office (formerly the Advanced Research and Development Activity) and REFLEX. George Miller and Christiane Fellbaum received the 2006 Antonio Zampolli Prize for their work with WordNet.

The Global WordNet Association is a non-commercial organization that provides a platform for discussing, sharing and connecting WordNets for all languages in the world. Christiane Fellbaum and Piek Th.J.M. Vossen are its co-presidents. [5]

Database contents

Example entry "Hamburger" in WordNet Hamburger WordNet.png
Example entry "Hamburger" in WordNet

The database contains 155,327 words organized in 175,979 synsets for a total of 207,016 word-sense pairs; in compressed form, it is about 12 megabytes in size. [6]

It includes the lexical categories nouns, verbs, adjectives and adverbs but ignores prepositions, determiners and other function words.

Words from the same lexical category that are roughly synonymous are grouped into synsets, which include simplex words as well as collocations like "eat out" and "car pool." The different senses of a polysemous word form are assigned to different synsets. A synset's meaning is further clarified with a short defining gloss and one or more usage examples. An example adjective synset is:

good, right, ripe – (most suitable or right for a particular purpose; "a good time to plant tomatoes"; "the right time to act"; "the time is ripe for great sociological changes")

All synsets are connected by means of semantic relations. These relations, which are not all shared by all lexical categories, include:

These semantic relations hold among all members of the linked synsets. Individual synset members (words) can also be connected with lexical relations. For example, (one sense of) the noun "director" is linked to (one sense of) the verb "direct" from which it is derived via a "morphosemantic" link.

The morphology functions of the software distributed with the database try to deduce the lemma or stem form of a word from the user's input. Irregular forms are stored in a list, and looking up "ate" will return "eat," for example.

Knowledge structure

Both nouns and verbs are organized into hierarchies, defined by hypernym or IS A relationships. For instance, one sense of the word dog is found following hypernym hierarchy; the words at the same level represent synset members. Each set of synonyms has a unique index.

At the top level, these hierarchies are organized into 25 beginner "trees" for nouns and 15 for verbs (called lexicographic files at a maintenance level). All are linked to a unique beginner synset, "entity". Noun hierarchies are far deeper than verb hierarchies.

Adjectives are not organized into hierarchical trees. Instead, two "central" antonyms such as "hot" and "cold" form binary poles, while 'satellite' synonyms such as "steaming" and "chilly" connect to their respective poles via a "similarity" relations. The adjectives can be visualized in this way as "dumbbells" rather than as "trees".

Psycholinguistic aspects

The initial goal of the WordNet project was to build a lexical database that would be consistent with theories of human semantic memory developed in the late 1960s. Psychological experiments indicated that speakers organized their knowledge of concepts in an economic, hierarchical fashion. Retrieval time required to access conceptual knowledge seemed to be directly related to the number of hierarchies the speaker needed to "traverse" to access the knowledge. Thus, speakers could more quickly verify that canaries can sing because a canary is a songbird, but required slightly more time to verify that canaries can fly (where they had to access the concept "bird" on the superordinate level) and even more time to verify canaries have skin (requiring look-up across multiple levels of hyponymy, up to "animal"). [7] While such psycholinguistic experiments and the underlying theories have been subject to criticism, some of WordNet's organization is consistent with experimental evidence. For example, anomic aphasia selectively affects speakers' ability to produce words from a specific semantic category, a WordNet hierarchy. Antonymous adjectives (WordNet's central adjectives in the dumbbell structure) are found to co-occur far more frequently than chance, a fact that has been found to hold for many languages.

As a lexical ontology

WordNet is sometimes called an ontology, a persistent claim that its creators do not make. The hypernym/hyponym relationships among the noun synsets can be interpreted as specialization relations among conceptual categories. In other words, WordNet can be interpreted and used as a lexical ontology in the computer science sense. However, such an ontology should be corrected before being used, because it contains hundreds of basic semantic inconsistencies; for example there are, (i) common specializations for exclusive categories and (ii) redundancies in the specialization hierarchy. Furthermore, transforming WordNet into a lexical ontology usable for knowledge representation should normally also involve (i) distinguishing the specialization relations into subtypeOf and instanceOf relations, and (ii) associating intuitive unique identifiers to each category. Although such corrections and transformations have been performed and documented as part of the integration of WordNet 1.7 into the cooperatively updatable knowledge base of WebKB-2, [8] most projects claiming to reuse WordNet for knowledge-based applications (typically, knowledge-oriented information retrieval) simply reuse it directly.

WordNet has also been converted to a formal specification, by means of a hybrid bottom-up top-down methodology to automatically extract association relations from it and interpret these associations in terms of a set of conceptual relations, formally defined in the DOLCE foundational ontology. [9]

In most works that claim to have integrated WordNet into ontologies, the content of WordNet has not simply been corrected when it seemed necessary; instead, it has been heavily reinterpreted and updated whenever suitable. This was the case when, for example, the top-level ontology of WordNet was restructured [10] according to the OntoClean-based approach, or when it was used as a primary source for constructing the lower classes of the SENSUS ontology.

Limitations

The most widely discussed limitation of WordNet (and related resources like ImageNet) is that some of the semantic relations are more suited to concrete concepts than to abstract concepts. [11] For example, it is easy to create hyponyms/hypernym relationships to capture that a "conifer" is a type of "tree", a "tree" is a type of "plant", and a "plant" is a type of "organism", but it is difficult to classify emotions like "fear" or "happiness" into equally deep and well-defined hyponyms/hypernym relationships.

Many of the concepts in WordNet are specific to certain languages and the most accurate reported mapping between languages is 94%. [12] Synonyms, hyponyms, meronyms, and antonyms occur in all languages with a WordNet so far, but other semantic relationships are language-specific. [13] This limits the interoperability across languages. However, it also makes WordNet a resource for highlighting and studying the differences between languages, so it is not necessarily a limitation for all use cases.

WordNet does not include information about the etymology or the pronunciation of words and it contains only limited information about usage. WordNet aims to cover most everyday words and does not include much domain-specific terminology.

WordNet is the most commonly used computational lexicon of English for word-sense disambiguation (WSD), a task aimed at assigning the context-appropriate meanings (i.e. synset members) to words in a text. [14] However, it has been argued that WordNet encodes sense distinctions that are too fine-grained. This issue prevents WSD systems from achieving a level of performance comparable to that of humans, 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. [15] [16] [17]

Offensive content

WordNet includes words that can be perceived as pejorative or offensive. [18] 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. [19] 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 [20] 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, [21] and in a Python package called NLTK. [22] Other more sophisticated WordNet-based similarity techniques include ADW, [23] whose implementation is available in Java. WordNet can also be used to inter-link other vocabularies. [24]

Interfaces

Princeton maintains a list of related projects [25] that includes links to some of the widely used application programming interfaces available for accessing WordNet using various programming languages and environments.

WordNet is connected to several databases of the Semantic Web. WordNet is also commonly reused via mappings between the WordNet synsets and the categories from ontologies. Most often, only the top-level categories of WordNet are mapped.

Global WordNet Association

The Global WordNet Association (GWA) [26] is a public and non-commercial organization that provides a platform for discussing, sharing and connecting wordnets for all languages in the world. The GWA also promotes the standardization of wordnets across languages, to ensure its uniformity in enumerating the synsets in human languages. The GWA keeps a list of wordnets developed around the world. [27]

Other languages

Projects such as BalkaNet and EuroWordNet made it feasible to create standalone wordnets linked to the original one. Two such projects were the Russian WordNet, patronized by Petersburg State University of Means of Communication [46] and led by S.A. Yablonsky, [47] and Russnet, [48] by Saint Petersburg State University.

Linked data

Distributions

WordNet Database is distributed as a dictionary package (usually a single file) for the following software:

See also

Related Research Articles

Word-sense disambiguation is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious.

<span class="mw-page-title-main">Glossary</span> Alphabetical list of terms relevant to a certain field of study or action

A glossary, also known as a vocabulary or clavis, is an alphabetical list of terms in a particular domain of knowledge with the definitions for those terms. Traditionally, a glossary appears at the end of a book and includes terms within that book that are either newly introduced, uncommon, or specialized. While glossaries are most commonly associated with non-fiction books, in some cases, fiction novels sometimes include a glossary for unfamiliar terms.

<span class="mw-page-title-main">Hypernymy and hyponymy</span> Semantic relations involving the type-of property

Hypernymy and hyponymy are the semantic relations between a generic term (hypernym) and a specific instance of it (hyponym). The hypernym is also called a supertype, umbrella term, or blanket term. The hyponym is a subtype of the hypernym. The semantic field of the hyponym is included within that of the hypernym. For example, pigeon, crow, and hen are all hyponyms of bird and animal; bird and animal are both hypernyms of pigeon, crow, and hen.

Lexical semantics, as a subfield of linguistic semantics, is the study of word meanings. It includes the study of how words structure their meaning, how they act in grammar and compositionality, and the relationships between the distinct senses and uses of a word.

Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature. The term semantic similarity is often confused with semantic relatedness. Semantic relatedness includes any relation between two terms, while semantic similarity only includes "is a" relations. For example, "car" is similar to "bus", but is also related to "road" and "driving".

<span class="mw-page-title-main">Semantic lexicon</span>

A semantic lexicon is a digital dictionary of words labeled with semantic classes so associations can be drawn between words that have not previously been encountered. Semantic lexicons are built upon semantic networks, which represent the semantic relations between words. The difference between a semantic lexicon and a semantic network is that a semantic lexicon has definitions for each word, or a "gloss".

In linguistics, troponymy is the presence of a 'manner' relation between two lexemes.

The sequence between semantic related ordered words is classified as a lexical chain. A lexical chain is a sequence of related words in writing, spanning narrow or wide context window. A lexical chain is independent of the grammatical structure of the text and in effect it is a list of words that captures a portion of the cohesive structure of the text. A lexical chain can provide a context for the resolution of an ambiguous term and enable disambiguation of concepts that the term represents.

Ontology learning is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between the concepts that these terms represent from a corpus of natural language text, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process.

Language resource management – Lexical markup framework, produced by ISO/TC 37, is the ISO standard for natural language processing (NLP) and machine-readable dictionary (MRD) lexicons. The scope is standardization of principles and methods relating to language resources in the contexts of multilingual communication.

In digital lexicography, natural language processing, and digital humanities, a lexical resource is a language resource consisting of data regarding the lexemes of the lexicon of one or more languages e.g., in the form of a database.

GermaNet is a semantic network for the German language. It relates nouns, verbs, and adjectives semantically by grouping lexical units that express the same concept into synsets and by defining semantic relations between these synsets. GermaNet is free for academic use, after signing a license. GermaNet has much in common with the English WordNet and can be viewed as an on-line thesaurus or a light-weight ontology. GermaNet has been developed and maintained at the University of Tübingen since 1997 within the research group for General and Computational Linguistics. It has been integrated into the EuroWordNet, a multilingual lexical-semantic database.

IndoWordNet is a linked lexical knowledge base of wordnets of 18 scheduled languages of India, viz., Assamese, Bangla, Bodo, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Malayalam, Meitei (Manipuri), Marathi, Nepali, Odia, Punjabi, Sanskrit, Tamil, Telugu and Urdu.

<span class="mw-page-title-main">BabelNet</span> Multilingual semantic network and encyclopedic dictionary

BabelNet is a multilingual lexicalized semantic network and ontology developed at the NLP group of the Sapienza University of Rome. BabelNet was automatically created by linking Wikipedia to the most popular computational lexicon of the English language, WordNet. The integration is done using an automatic mapping and by filling in lexical gaps in resource-poor languages by using statistical machine translation. The result is an encyclopedic dictionary that provides concepts and named entities lexicalized in many languages and connected with large amounts of semantic relations. Additional lexicalizations and definitions are added by linking to free-license wordnets, OmegaWiki, the English Wiktionary, Wikidata, FrameNet, VerbNet and others. Similarly to WordNet, BabelNet groups words in different languages into sets of synonyms, called Babel synsets. For each Babel synset, BabelNet provides short definitions in many languages harvested from both WordNet and Wikipedia.

Automatic taxonomy construction (ATC) is the use of software programs to generate taxonomical classifications from a body of texts called a corpus. ATC is a branch of natural language processing, which in turn is a branch of artificial intelligence.

plWordNet is a lexico-semantic database of the Polish language. It includes sets of synonymous lexical units (synsets) followed by short definitions. plWordNet serves as a thesaurus-dictionary where concepts (synsets) and individual word meanings are defined by their location in the network of mutual relations, reflecting the lexico-semantic system of the Polish language. plWordNet is also used as one of the basic resources for the construction of natural language processing tools for Polish.

The Bulgarian WordNet (BulNet) is an electronic multilingual dictionary of synonym sets along with their explanatory definitions and sets of semantic relations with other words in the language.

UBY is a large-scale lexical-semantic resource for natural language processing (NLP) developed at the Ubiquitous Knowledge Processing Lab (UKP) in the department of Computer Science of the Technische Universität Darmstadt . UBY is based on the ISO standard Lexical Markup Framework (LMF) and combines information from several expert-constructed and collaboratively constructed resources for English and German.

<span class="mw-page-title-main">Arabic Ontology</span> Linguistic ontology

Arabic Ontology is a linguistic ontology for the Arabic language, which can be used as an Arabic WordNet with ontologically clean content. People use it also as a tree of the concepts/meanings of the Arabic terms. It is a formal representation of the concepts that the Arabic terms convey, and its content is ontologically well-founded, and benchmarked to scientific advances and rigorous knowledge sources rather than to speakers' naïve beliefs as wordnets typically do . The Ontology tree can be explored online.

OntoLex is the short name of a vocabulary for lexical resources in the web of data (OntoLex-Lemon) and the short name of the W3C community group that created it.

References

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  2. ↑ "WordNet Search - 3.1".
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  4. ↑ "WordNets in the World". Global WordNet Association. Retrieved 19 January 2020.
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  67. ↑ "Lingoes WordNet". Lingoes.net. 2007-11-16. Retrieved 2014-03-11.
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