Reference · Language & intelligence

NLP & linguistics

34 functions in 26 classes, grouped by what they do, each with the explanation the library gives of itself.

The story of this area, for a general reader: what a crafter does with it, an example run and its rated lanes, is on its Platform page.

The functions, by what they do

Testing and checking

IsNegative TRUE if the sentiment score is -0.05 or below. in stzText
composed example
```
o1 = new stzText("Alice met Bob in Paris. The weather was fine.")
? o1.IsNegative()
#--> 0
```
composed from the signature, run for this page
IsPositive TRUE if the sentiment score reaches +0.05. in stzText
composed example
```
o1 = new stzText("Alice met Bob in Paris. The weather was fine.")
? o1.IsPositive()
#--> 1
```
composed from the signature, run for this page
IsStopword TRUE if the text is a stopword. in stzText
composed example
```
o1 = new stzText("Alice met Bob in Paris. The weather was fine.")
? o1.IsStopword()
#--> 0
```
composed from the signature, run for this page
HasSynonyms wordnet (synonyms + hypernyms) in stzText
composed example
```
o1 = new stzText("Alice met Bob in Paris. The weather was fine.")
? o1.HasSynonyms()
#--> 0
```
composed from the signature, run for this page
IsSynonymOf wordnet (synonyms + hypernyms) in stzText
composed example
```
o1 = new stzText("Alice met Bob in Paris. The weather was fine.")
? o1.IsSynonymOf("an")
#--> 0
```
composed from the signature, run for this page
HasSemanticModel Upgrades text similarity from lexical bag-of-words to true MEANING via a runtime neural model (load one process-wide with StzNeuralModelQ(path)). With no model loaded these degrade gracefully to the lexical path, so code keeps working and auto-improves once a model is present. TRUE if a runtime embedding model is loaded and ready. in stzText
composed example
```
o1 = new stzText("Alice met Bob in Paris. The weather was fine.")
? o1.HasSemanticModel()
#--> 0
```
composed from the signature, run for this page
IsSemanticallySimilarTo semantic layer (neural embeddings) in stzText
composed example
```
o1 = new stzText("Alice met Bob in Paris. The weather was fine.")
? o1.IsSemanticallySimilarTo("an", 2)
#--> 0
```
composed from the signature, run for this page
Is copula, CLOSING form: "What the first char of Haro IS" in stzQuestion
IsAQ "Suppose X IS A fruit" -- records the assumption in the overlay in stzSupposition

Counting and measuring

NumberOfWords vocabulary & frequency in stzCorpus
composed example
```
o1 = new stzCorpus("seed document about cats")
? o1.NumberOfWords()
#--> 4
```
composed from the signature, run for this page
NumberOfDocumentsWith TF-IDF (the vectorizer feeding the ML floor) in stzCorpus
composed example
```
o1 = new stzCorpus("seed document about cats")
? o1.NumberOfDocumentsWith("an")
#--> 0
```
composed from the signature, run for this page
NumberOfQ NO mid-chain IsQ() and NO NotQ() -- both were linguistically wrong (author's ruling): the sentence-initial IsQ() IS the fronted copula (subject-aux inversion), so a second one doubles it; and English negates comparisons with ANTONYM comparators (DifferentFromQ), predicates with negative FORMS (IsNot*/DoesNot*), and questions with the TAG ("..., or not?" -> OrNot()). "the NUMBER OF <noun>" as a constituent: count mode for this side in stzQuestion

Replacing and setting

SetWord construction (the parser builds bottom-up) in stzParseTree
SetApproximate Label agreement measured 1.000 in both rows above -- on separable data the vote absorbed every neighbour the forest missed. Do not read that as a guarantee; read it as the reason AgreementWithExact() takes YOUR queries. in stzKnn
SetApproximateTrees More trees, better recall, bigger index and a slower build. in stzKnn
SetApproximateBudget Candidates examined per query before the true distances decide. 0 lets the engine scale it from k. This is the recall dial. in stzKnn
SetChainToReturn controlling chain process in stzChainOfTruth

Removing

WithoutStopwords The text with the stopwords removed. in stzText

composed example

o1 = new stzText("Alice met Bob in Paris. The weather was fine.")
? o1.WithoutStopwords()
#--> Alice met Bob Paris weather fine

composed from the signature, run for this page

StripEdgePunct A word without clinging trailing punctuation ("empty?" -> "empty"). BYTE-based whole-mark matching (StzLeft/StzRight take byte counts while StzLen counts codepoints -- mixing them mangles multibyte words; exact byte-suffix comparison is UTF-8-safe). in stzNaturalEngine
composed example
```
o1 = new stzNaturalEngine("en", "", [], "")
? o1.StripEdgePunct("a")
#--> a
```
composed from the signature, run for this page

Adding

AddGolden goldens in stzDLM

Splitting and extracting

ExtractParamsBackward VERB-FINAL grammar: the parameters precede their verb ("'.'" instead- of "'_'" put). Walk backward to the previous action/creation boundary collecting unconsumed literals, keep the LAST nParamCount in textual order, and mark them consumed so no later verb re-grabs them. in stzNaturalEngine
Split deterministic head/tail split -- [ oTrain, oTest ] in stzTrainingSet

Converting

ParseTree part-of-speech tagging (penn treebank) in stzText