Exhibit 07Classification
Part-of-Speech Tagging
Every word in a sentence gets one of 49 grammatical tags. It is the textbook classification problem with real, human-annotated answers, and the kind of many-label choice Jev is built for.
In scopeMany narrow classification judgments, each choosing from dozens of labels: Jev's headline use case. TypeSafe launch blog: “Jev supports a cardinality up to 255.”
World
→State
→Decision
→Action
→New state
1 / 200
There are currently 5 locations:
gold = human annotationjevhmm = textbook HMM + Viterbimft = most frequent tag
This session
Tag a few test sentences to build up accuracy against the human annotation.
On all 2,593 tokens of the 200 test sentences here, the textbook taggers score 92% (HMM) and 88% (most frequent tag), trained on the UD English EWT r2.18 (commit b7711cc) training split.
Tag families
nounsverbsadjectivesadverbspronounsdeterminersprepositions & conjunctionsnumberspunctuationother
All 49 tags
- NNNoun, singular or mass: dog, water, idea
- NNSNoun, plural: dogs, ideas
- NNPProper noun, singular: Google, London, Monday
- NNPSProper noun, plural: Americans, Alps
- VBVerb, base form: to go, can see, please help
- VBDVerb, past tense: went, saw, morphed
- VBGVerb, gerund or present participle: going, seeing
- VBNVerb, past participle: gone, seen, has taken
- VBPVerb, present tense, not 3rd person singular: I go, they are
- VBZVerb, present tense, 3rd person singular: she goes, it is
- MDModal verb: can, will, should, might
- JJAdjective: big, green, important
- JJRAdjective, comparative: bigger, better
- JJSAdjective, superlative: biggest, best
- RBAdverb: quickly, not, very, also
- RBRAdverb, comparative: faster, more (as adverb)
- RBSAdverb, superlative: fastest, most (as adverb)
- RPParticle of a phrasal verb: give UP, turn OFF
- WRBWh-adverb: where, when, why, how
- PRPPersonal pronoun: I, you, he, it, them
- PRP$Possessive pronoun before a noun: my, your, their
- WPWh-pronoun: who, what, whom
- WP$Possessive wh-pronoun: whose
- EXExistential there: THERE is a problem
- DTDeterminer: the, a, this, some, no
- PDTPredeterminer before a determiner: ALL the, BOTH his
- WDTWh-determiner: which, that (introducing a relative clause)
- INPreposition or subordinating conjunction: in, of, if, because
- TOThe word to, as infinitive marker or preposition
- CCCoordinating conjunction: and, but, or
- POSPossessive ending: the 's in John's
- CDCardinal number: 3, three, 2004, 1.5
- LSList item marker: 1), a., (b) at the start of an item
- .Sentence-final punctuation: . ! ?
- ,Comma: ,
- :Colon, semicolon, dash or ellipsis inside a sentence: : ; -- ...
- ``Opening quotation mark: “ or " at the start of a quote
- ''Closing quotation mark: ” or " at the end of a quote
- -LRB-Opening bracket: ( [ {
- -RRB-Closing bracket: ) ] }
- HYPHHyphen joining parts of a split word: the - in e - mail
- NFPSuperfluous or decorative punctuation: *** ~~ :)
- $Currency symbol: $ £ €
- SYMSymbol used as a word: % & = + /
- UHInterjection: yes, oh, thanks, hello
- FWForeign word: et, al, bon, voyage
- ADDEmail address or URL: www.example.com
- GWFragment of a word split by a typo: the 'every' of 'every thing'
- AFXSeparated affix or prefix: pre, anti (as in pre - war)
Jev decision
idlePress Tag with Jev. Each token becomes one question with all 49 tags as options; up to 16 questions go in one call. Click a token to see Jev's full distribution.
NN · Noun, singular or mass—
NNS · Noun, plural—
NNP · Proper noun, singular—
NNPS · Proper noun, plural—
Other (45)—
Lesson · Classification
Word in context → one of 45 tags
Part-of-speech tagging is a long line of narrow classification decisions, each choosing from dozens of labels, which is what TypeSafe says Jev is for. Unlike the Restaurant exhibit, there is a right answer: every test sentence was tagged by human annotators (Universal Dependencies English EWT). The classic solutions are the textbook ones: tag each word with its most common tag in the training data, or run a hidden Markov model with the Viterbi algorithm so neighbouring tags inform each other. Both are trained on the same corpus; Jev is not trained on it and sees each sentence cold, one question per word.