entity stringclasses 4
values | primes stringclasses 4
values | embedding stringclasses 4
values |
|---|---|---|
adjective | part of speech that describes a noun or pronoun,property,part of speech,part,nominal,adjectives,word,noun,speech,pronoun | [-0.0012560616014525294, -0.022227883338928223, -0.009745020419359207, 0.018132517114281654, 0.019931092858314514, -0.03898922726511955, 0.012433301657438278, 0.01397140696644783, 0.005819152109324932, 0.02772156335413456, 0.050468649715185165, 0.000988591113127768, 0.012921587564051151, -0.03460510075092316, -0.003998... |
verb | state,verbs,occurrence,auxiliary verb, contain the notions of action,action,content word,notions,function,word order, from the semantic point of view,part of speech,word, process or state, exert the core function of the sentence predicate.,words,sentence,core,view,process, from the syntactic point of view, and,class of... | [-0.01781720668077469, -0.022800635546445847, -0.011292855255305767, 0.016039634123444557, -0.021923383697867393, -0.01064459141343832, -0.015112200751900673, 0.027735859155654907, 0.008178235962986946, 0.037463463842868805, 0.026538530364632607, 0.01660086028277874, 0.011499619111418724, -0.01854262501001358, -0.00268... |
adverb | adjective,adverb,content word,part of speech,condition,word, or another adverb,verb,word that modifies a verb | [-0.005304735153913498, -0.02259357087314129, -0.02525949478149414, 0.03003602661192417, -0.0012312920298427343, -0.03806686028838158, 0.010841279290616512, 0.005346125457435846, 0.018509898334741592, 0.0313434861600399, 0.034636013209819794, -0.003393976716324687, 0.019249049946665764, -0.05315723642706871, 0.01091469... |
noun | substance,part of speech,set,objects,object,word,nominal locution,word that functions as the name of a specific object or set of objects,functions,name,noun phrase | [-0.0038496803026646376, -0.01698467694222927, 0.0013867357047274709, 0.011925791390240192, -0.0068103899247944355, -0.019722674041986465, 0.014320810325443745, 0.0009361974662169814, 0.012890549376606941, 0.036402955651283264, 0.06349235028028488, -0.004097110591828823, -0.011813187971711159, -0.0415031723678112, -0.0... |
WordNet Semantic Primes
Dataset Overview
We propose a dataset at the core of our semantic towers methodology which combines vectorized knowledge graph information to augment a Retrieval-and-Generation (RAG) pipeline.
Dataset Construction
The dataset is constructed by deriving and building the semantic tower - an ensemble of primitive semantic information related to a term - of 4 term types (noun, verb, adverb, adjective). These term typed are derived from a data dump from the original WordNet dataset.
The semantic tower encompasses information gathered from Wikidata, specifically:
- label
- instance of
- subclass of
- part of
- represents
- description
This information forms the smallest subset of knowledge needed to distinguish a term from another.
Embeddings Generation
The vector embeddings are generated using the General Text Embeddings (GTE) large model.
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