![]() However, it is necessary to install three dependencies: babelnet-api, jlt, and jwi. This software uses BabelNet API of version 2.5.1 that is not distributed via Maven. "/opt/WordNet-3.1/")Ĥ) run `install-deps.sh` to install the BabelNet JARs into your local Maven repository.Įxecute linker2BN.sh FOLDER FILE ITERATIONSĮxecute linker2WN.sh FOLDER FILE ITERATIONS WORDNETFOLDER BabelNet Dependencies be seen as a combination and extension of a dictionary and thesaurus. SerializeThesaurus.sh FOLDER GZIPPEDTHESAURUSĢ) BabeblNet following the instructions from download the Java API and the lastest index distribution.Ĭopy both the API jar and the "config" folder into the project folder "dist/lib/" ģ) WordNet download the lastest WordNet distribution from and install the resource in some specific folder WORDNETFOLDER (e.g. WordNet is a lexical database of semantic relations between words that links words into. In order to correctly execute the linking procedure please follow this three steps: We provide the source code for the linking with BabelNet and WordNet. Finally, to obtain a truly unified resource, we link the “orphan” PCZ senses for which no corresponding sense could be found by inferring their type in the LR. That is, we create a mapping between the two sense inventories and then combine them into a new extended sense inventory, our hybrid aligned resource. Linking to a lexical resource: we align the PCZ with an existing lexical resource (LR). BabelNet is a very large automatically generated multilingual thesaurus 5, the Russian part of which consists of 1.84M lemmas, 985K synsets.In contrast to a term-based distributional thesaurus (DT), a PCZ consists of sense-disambiguated entries, i.e., all terms have a sense identifier. The result is a proto-conceptualization (PCZ). Disambiguation of related words: we fully disambiguate all lexical information associated with a proto-concept, i.e., similar terms and hypernyms, based on the partial disambiguation from the previous step.Learning a JoBimText model: initially, we automatically create a sense inventory from a large text collection using the pipeline of the JoBimText project.Our approach consists of three main phases: Manual evaluation based on human judgments indicates the high quality of the resource, as well as the benefits of enriching top-down lexical knowledge resources with bottom-up distributional information from text. In contrast to dense vector representations, our resource is human readable and interpretable, and can be easily embedded within the Semantic Web ecosystem. ![]() In conclusion, the possible causes of errors of disambiguation systems are described and a solution to improve them is proposed.Linked Disambiguated Distributional Semantic Networksĭisambiguated Distributional Semantic-based Sense Inventories are hybrid knowledge bases that combines the contextual information of distributional models with the conciseness and precision of manually constructed lexical networks. According to the statistical analysis of errors it can be concluded, that the quality of work of systems for removing ambiguity is not high enough. During the experiment, the quality of the work of the systems was evaluated. The testing was conducted using several sentences containing ambiguous words, expressions, phrasal verbs, homonyms and other ambiguous constructions. Recently, BabelNet, a multilingual encyclopedic dictionary. The Lesk algorithm runs on the NLTK library and software package and Babelfy is based on the Babelnet semantic network. Other resources used for disambiguation purposes include Rogets Thesaurus and Wikipedia. ![]() The systems belong to different approaches. The article describes the experiment of testing systems of word sense disambiguation – the Lesk algorithm and Babelfy system. Nowadays the task of qualitative removing of ambiguity is still not solved, nevertheless, several approaches to word sense disambiguation are available. Disambiguation is a relevant scientific field of research in language theory and natural language processing. ![]()
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