How a language model learns: guessing the next word, sentence 15
Picture-making AI learned from millions of pictures with descriptions and builds new pictures that fit the words.
Who acts: making AI, new pictures · What happens: make, learn, build, fit · To what, where: millions, new pictures, words
This is a sentence of the lesson «How a language model learns: guessing the next word» of the ai course of Mova's primary school. The page gives its syntax tree — a dependency tree in the style of Universal Dependencies — and its pragmatics clause by clause: who acts (making AI, new pictures), what happens, when and where, as Mova's frames read it from the tree.
The tree
The words, one row each (CoNLL-U)
| # | word | lemma | UPOS | features | head | relation |
|---|---|---|---|---|---|---|
| 1 | Picture | picture | NOUN | Number=Sing | 3 making | compound |
| 2 | - | - | PUNCT | _ | 1 Picture | punct |
| 3 | making | make | VERB | VerbForm=Ger | 4 AI | amod |
| 4 | AI | ai | NOUN | Number=Sing | 5 learned | nsubj |
| 5 | learned | learn | VERB | Mood=Ind Number=Sing Person=3 Tense=Past VerbForm=Fin | root | root |
| 6 | from | from | ADP | _ | 7 millions | case |
| 7 | millions | million | NOUN | Number=Plur | 5 learned | obl |
| 8 | of | of | ADP | _ | 9 pictures | case |
| 9 | pictures | picture | NOUN | Number=Plur | 7 millions | nmod |
| 10 | with | with | ADP | _ | 11 descriptions | case |
| 11 | descriptions | description | NOUN | Number=Plur | 9 pictures | nmod |
| 12 | and | and | CCONJ | _ | 13 builds | cc |
| 13 | builds | build | VERB | Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin | 5 learned | conj |
| 14 | new | new | ADJ | Degree=Pos | 15 pictures | amod |
| 15 | pictures | picture | NOUN | Number=Plur | 13 builds | obj |
| 16 | that | that | PRON | PronType=Rel | 17 fit | nsubj |
| 17 | fit | fit | VERB | Mood=Ind Number=Plur Person=3 Tense=Pres VerbForm=Fin | 15 pictures | acl:relcl |
| 18 | the | the | DET | Definite=Def PronType=Art | 19 words | det |
| 19 | words | word | NOUN | Number=Plur | 17 fit | obj |
| 20 | . | . | PUNCT | _ | 5 learned | punct |
Pragmatics
No stored speech act for this sentence.
| # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by |
|---|---|---|---|---|---|---|---|---|---|
| 1 | make | gerund | vn build-26.1-1 «NP.asset V NP»: agent=Asset | ||||||
| 2 | learn | past | making AI | millions of pictures with descriptions | vn learn-14-2-1 «NP V PP.source»: agent=Agent from million=Source | ||||
| 3 | build | present | making AI not said here | new pictures | vn build-26.1-1 «NP.asset V NP»: agent=Asset picture=Product | ||||
| 4 | fit | present | new pictures | the words | vn adjust-26.9 «NP V NP»: agent=Agent word=Patient |
One row per clause the reader finds. «not said here»: a role filled from the clause above. «roles by»: a VerbNet class and frame when one fits the verb, else the reader's rules on the tree.
Licence: CC BY-SA 4.0, as the whole of Mova corpus 0.4, of which this treebank (the agreed school, mova-school-agreed-en.conllu) is part. The lessons were written for Mova's primary school. Sentence school:ai:002-guessing-the-next-word.lesson:15.
The tree: Mova annot: Rust (en) → Haiku 5.5 (medium, Batches API) patches; gates UD 2.18; agreed by treeagree rules (Stanza, Trankit, spaCy trf): no change. The frames are read from the tree by Mova's frames reader (prag) when this page is built, the lesson read in order, so a pronoun may be resolved from an earlier sentence.
See the release 0.4.
This page in Markdown → https://md.mova.elamur.ai/free/en/corpora/school/ai/2-guessing-the-next-word/15/