--- title: The text a bot learned from had good and bad parts; companies filter… — How a language model learns: guessing the next w… · tree and pragmatics description: The text a bot learned from had good and bad parts; companies filter it and train the bot to be polite, but they do not catch everything. Who acts: bot, text, companies. A sentence of the lesson «How a language model learns: guessing the next word» of the ai course of Mova's primary school with its --- [Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/corpora/school/ai/2-guessing-the-next-word/13/) [home](https://md.mova.elamur.ai/free/en/) / [corpora](https://md.mova.elamur.ai/free/en/corpora/) / [school](https://md.mova.elamur.ai/free/en/corpora/school/) / [AI](https://md.mova.elamur.ai/free/en/corpora/school/ai/) / [How a language model learns: guessing…](https://md.mova.elamur.ai/free/en/corpora/school/ai/2-guessing-the-next-word/) / 13 # How a language model learns: guessing the next word, sentence 13 The text a bot learned from had good and bad parts; companies filter it and train the bot to be polite, but they do not catch everything. **Who acts:** bot, text, companies · **What happens:** learn, have, filter, train, be, not catch · **To what, where:** text, good parts, bot, everything 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 (bot, text, companies), what happens, when and where, as Mova's frames read it from the tree. ## The tree {#tree} ```conllu # sent_id = school:ai:002-guessing-the-next-word.lesson:13 # text = The text a bot learned from had good and bad parts; companies filter it and train the bot to be polite, but they do not catch everything. # source = school lesson (ours) # course = ai # lesson = 002-guessing-the-next-word.lesson # annotation = Mova annot: Rust (en) → Haiku 5.5 (medium, Batches API) patches; gates UD 2.18; agreed by treeagree rules (Stanza, Trankit, spaCy trf): R8-pos-agree ×3 1 The the DET DT Definite=Def|PronType=Art 2 det _ _ 2 text text NOUN NN Number=Sing 7 nsubj _ _ 3 a a DET DT Definite=Ind|PronType=Art 4 det _ _ 4 bot bot NOUN NN Number=Sing 5 nsubj _ _ 5 learned learn VERB VBD Mood=Ind|Number=Sing|Person=3|Tense=Past|VerbForm=Fin 2 acl _ Agree=R8-pos-agree|Haiku=2:acl|HaikuPos=VERB:VBN:Tense=Past|VerbForm=Part|Voice=Pass 6 from from ADP IN _ 2 case _ _ 7 had have VERB VBD Mood=Ind|Tense=Past|VerbForm=Fin 0 root _ _ 8 good good ADJ JJ Degree=Pos 11 amod _ _ 9 and and CCONJ CC _ 10 cc _ _ 10 bad bad ADJ JJ Degree=Pos 8 conj _ _ 11 parts part NOUN NNS Number=Plur 7 obj _ _ 12 ; ; PUNCT , _ 14 punct _ _ 13 companies company NOUN NNS Number=Plur 14 nsubj _ _ 14 filter filter VERB VBP Mood=Ind|Number=Plur|Person=3|Tense=Pres|VerbForm=Fin 7 parataxis _ Agree=R8-pos-agree|Haiku=7:parataxis|HaikuPos=VERB:VB:VerbForm=Inf 15 it it PRON PRP Case=Acc|Gender=Neut|Number=Sing|Person=3|PronType=Prs 14 obj _ _ 16 and and CCONJ CC _ 17 cc _ _ 17 train train VERB VBP Mood=Ind|Number=Plur|Person=3|Tense=Pres|VerbForm=Fin 14 conj _ Agree=R8-pos-agree|Haiku=14:conj|HaikuPos=VERB:VB:VerbForm=Inf 18 the the DET DT Definite=Def|PronType=Art 19 det _ _ 19 bot bot NOUN NN Number=Sing 17 obj _ _ 20 to to PART TO _ 22 mark _ _ 21 be be AUX VB VerbForm=Inf 22 cop _ _ 22 polite polite ADJ JJ Degree=Pos 17 xcomp _ _ 23 , , PUNCT , _ 28 punct _ _ 24 but but CCONJ CC _ 28 cc _ _ 25 they they PRON PRP Case=Nom|Number=Plur|Person=3|PronType=Prs 28 nsubj _ _ 26 do do AUX VBP Mood=Ind|Number=Plur|Person=3|Tense=Pres|VerbForm=Fin 28 aux _ _ 27 not not PART RB Polarity=Neg 28 advmod _ _ 28 catch catch VERB VB VerbForm=Inf 14 conj _ _ 29 everything everything PRON NN Number=Sing|PronType=Tot 28 obj _ _ 30 . . PUNCT . _ 7 punct _ _ ``` ## Pragmatics {#pragmatics} - form: statement - act: narrate - voice: narrator - polarity: neg - teacher: kept the draft | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **learn** | past | a bot | the text *not said here* | | | | | vn learn-14-2-1 «NP V NP»: agent=Agent text=Topic | | 2 | **have** | past | the text | good parts | | | | | vn birth-28.2-1 «NP V NP»: agent=Agent part=Patient | | 3 | **filter** | present | companies | bot | | | | | vn wipe\_instr-10.4.2 «NP V NP.initial\_location»: agent=Agent it=Initial\_Location | | 4 | **train** | present | companies *not said here* | the bot | | | | | vn hire-13.5.3 «NP V NP»: agent=Agent bot=Theme | | 5 | **is** | to-infinitive | the bot *not said here* | | | | | polite *property* | tree rules | | 6 | **not** **catch** | present · do-support | companies *= company* | everything | | | | | vn get-13.5.1 «NP V NP»: agent=Agent everything=Theme | 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](https://creativecommons.org/licenses/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:13`. **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): R8-pos-agree ×3. **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](https://md.mova.elamur.ai/free/en/releases/0.4/). - ← previous: [How a language model learns: guessing…, 12](https://md.mova.elamur.ai/free/en/corpora/school/ai/2-guessing-the-next-word/12/) - next →: [How a language model learns: guessing…, 14](https://md.mova.elamur.ai/free/en/corpora/school/ai/2-guessing-the-next-word/14/) - ↑ back to [the lesson](https://md.mova.elamur.ai/free/en/corpora/school/ai/2-guessing-the-next-word/)