44 VERB verb VB verb, base form VerbForm=inf I want to **go**. 42 VERB verb HVS forms of “have” I**’ve** seen the Queen 43 VERB verb MD verb, modal auxiliary VerbType=mod This **could** work. 39 SYM symbol $ symbol, currency SymType=currency Dollar **$** is the name of more than 20 curre… 40 SYM symbol SYM symbol this is a symbol **$** 41 VERB verb BES auxiliary “be” Let it **be**. ai 36 PUNCT punctuation LS list item marker NumType=ord 37 PUNCT punctuation NFP superfluous punctuation 38 SYM symbol # symbol, number sign SymType=numbersign This is hash**#** symbol. punctuation mark, sentence closer PunctType=peri Punctuation at the end of sentence**.** 32 PUNCT punctuation ” closing quotation mark PunctType=quot PunctSide=fin “ machine learning**”** 33 PUNCT punctuation “” closing quotation mark PunctType=quot PunctSide=fin **””** 34 PUNCT punctuation “ opening quotation mark PunctType=quot PunctSide=ini **”**machine learning” 35 PUNCT punctuation HYPH punctuation mark, hyphen PunctType=dash ML site **-** machinelearningknowledge. 30 PUNCT punctuation : punctuation mark, colon or ellipsis colon **:** is a punctuation mark 31 PUNCT punctuation. 27 PUNCT punctuation -LRB- left round bracket PunctType=brck PunctSide=ini rounded brackets **(**also called parentheses) 28 PUNCT punctuation -RRB- right round bracket PunctType=brck PunctSide=fin rounded brackets (also called parentheses**)** 29 PUNCT punctuation, punctuation mark, comma PunctType=comm I**,**me and myself. 26 PROPN proper noun NNPS noun, proper plural NounType=prop Number=plur The **Flintstones** were a pre-historic family. 25 PROPN proper noun NNP noun, proper singular NounType=prop Number=sign **Kilroy** was here. 24 PRON pronoun PRP pronoun, personal PronType=prs **I** want **you** to go. 22 PART particle RP adverb, particle Put it **back**! 23 PART particle TO infinitival to PartType=inf VerbForm=inf I want **to** go. 21 PART particle POS possessive ending Poss=yes Fred**’s** name is short. 19 NOUN noun WP wh-pronoun, personal PronType=int rel **Who** was that? 20 NUM numeral CD cardinal number NumType=card I want **three** things. 18 NOUN noun NNS noun, plural Number=plur These are **words**. 17 NOUN noun NN noun, singular or mass Number=sing This is a **sentence**. 16 INTJ interjection UH interjection **Um**, I don’t know. 15 DET determiner DT determiner **This** is **a** sentence. 13 ADV adverb WRB wh-adverb PronType=int rel **When** was that? 14 CONJ conjunction CC conjunction, coordinating ConjType=coor The balloon popped **and** everyone jumped. 12 ADV adverb RBS adverb, superlative Degree=sup He ran **fastest**. 11 ADV adverb RBR adverb, comparative Degree=comp He ran **quicker**. 10 ADV adverb RB adverb Degree=pos He ran **quickly**. 9 ADV adverb EX existential there AdvType=ex **There** is cake. 8 ADP adposition IN conjunction, subordinating or preposition It arrived **in** a box. 7 ADJ adjective WP$ wh-pronoun, possessive Poss=yes PronType=int rel We don’t know **whose** it is. 6 ADJ adjective WDT wh-determiner PronType=int rel It’s blue, **which** is odd. 5 ADJ adjective PRP$ pronoun, possessive PronType=prs Poss=yes **His** arm hurts. 4 ADJ adjective PDT predeterminer AdjType=pdt PronType=prn Waking up is **half** the battle. 3 ADJ adjective JJS adjective, superlative Degree=sup This is the **best** sentence. 2 ADJ adjective JJR adjective, comparative Degree=comp This is a **better** sentence. 1 ADJ adjective JJ adjective Degree=pos This is a **good** sentence. POS POS_Description Fine-grained Tag Description Morphology EXAMPLE 0 ADJ adjective AFX affix Hyph=yes The Flintstones were a **pre**-historic family. The column parameter can be used to choose between the universal tags ( -column 3, default) or the EAGLES standard tags ( -column 4).īoth the property file ( ) and the resulting model ( ) are included in the Tint distribution as resources.Below is a POS tag list, their description, Fine-grained Tag, their description, Morphology, and some examples. v,-version display version information and terminateįor example, you can run it on the training set of the Universal Dependencies by using: h,-help display this help message and terminate After that, you need to run the eu.fbk.dh. class to read the CoNLLU format and transform it to the underscore-separated format. The ISTD dataset needs to be downloaded from the Universal Dependencies website. For example:Įvacuata_VERB la_DET Tate_PROPN Gallery_PROPN. The words should be tagged by having the word and the tag separated by the underscore character. In order to retrain the POS tagger using the ISTD dataset, you need to convert the original dataset to the format accepted by the Stanford MaxentTagger. You can surf to its FAQ page for more information. The Tint module for Part-of-speech tagging relies on the corresponding module in Stanford CoreNLP.
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