國立虎尾科技大學 |

Learning Hidden Structure : = Derived Environment Effects and the Richness of the Base.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Learning Hidden Structure :/
其他題名:	Derived Environment Effects and the Richness of the Base.
作者:	Tan, Adeline R.
面頁冊數:	1 online resource (244 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-09, Section: A.
Contained By:	Dissertations Abstracts International85-09A.
標題:	Sociolinguistics. -
電子資源:	click for full text (PQDT)
ISBN:	9798381968682

Learning Hidden Structure : = Derived Environment Effects and the Richness of the Base.
Tan, Adeline R.

Learning Hidden Structure :Derived Environment Effects and the Richness of the Base. - 1 online resource (244 pages)

Source: Dissertations Abstracts International, Volume: 85-09, Section: A.

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

Includes bibliographical references

Hidden structure refers to the units of organization that a child cannot directly observe when they are learning language (e.g. phonemes, morpheme boundaries, URs, phrases). In this dissertation, I propose a novel computational model that learns hidden structures in-tandem with the grammar. My model consists of two Maximum Entropy sub-models that are chained via the product rule. Since I treat the hidden structure as a latent variable, the learner is free to match the observed surface pattern via different intermediary URs. Latent variable models have no guarantee of concavity, so I develop a novel sampling technique to simulate a population of language learners.When presented with the same surface information, different human learners may arrive at different analyses (i.e. inter-speaker variation in analyses). In a production task with the -ity suffix and nonce stems, Pierrehumbert (2006) found that 2 in 10 participants never applied velar softening. This suggests that approximately 20% of learners may not learn the grammar for velar softening, but may instead memorize full underlying forms (e.g. /ɪlɛktɹɪsɪti/) for existing words. For velar softening, my model not only correctly predicts that there are multiple solutions for one surface pattern, it also correctly predicts the proportion of human speakers that will pick each solution.In the Rich Base problem, there are two grammars that satisfy one surface pattern. However, humans only acquire one grammar - the Rich Base Grammar (as evidenced by loan word adaptation). In my simulated population of language learners, I find an overwhelming preference for the Rich Base Grammar to be learned. This preference emerges from my model's ability to leverage the superior utility of the Rich Base Grammar over its non-Rich Base counterpart (without needing to build in any extra mechanisms or biases).English CiV lengthening appears at first blush to be a derived environment effect, whose triggering condition - the derived environment - cannot be directly observed. My experiments confirm the productivity of CiV lengthening. I reanalyze CiV lengthening as the emergence of the unmarked Stress-to-Weight Principle, thus simplifying CiV Lengthening from a complex hidden structure problem to a surface true phenomenon.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381968682Subjects--Topical Terms:

555401
Sociolinguistics.
Subjects--Index Terms:

Emergence of the unmarkedIndex Terms--Genre/Form:

554714
Electronic books.

Learning Hidden Structure : = Derived Environment Effects and the Richness of the Base.
LDR:03703ntm a22003977 4500 001 1152751
005 20241213095551.5
006 m o d
007 cr mn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798381968682
035 $a (MiAaPQ)AAI31140695
035 $a AAI31140695
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Tan, Adeline R. $3 1479834
245 1 0 $a Learning Hidden Structure : $b Derived Environment Effects and the Richness of the Base.
264 0 $c 2024
300 $a 1 online resource (244 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-09, Section: A.
500 $a Advisor: Zuraw, Kie Ross.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2024.
504 $a Includes bibliographical references
520 $a Hidden structure refers to the units of organization that a child cannot directly observe when they are learning language (e.g. phonemes, morpheme boundaries, URs, phrases). In this dissertation, I propose a novel computational model that learns hidden structures in-tandem with the grammar. My model consists of two Maximum Entropy sub-models that are chained via the product rule. Since I treat the hidden structure as a latent variable, the learner is free to match the observed surface pattern via different intermediary URs. Latent variable models have no guarantee of concavity, so I develop a novel sampling technique to simulate a population of language learners.When presented with the same surface information, different human learners may arrive at different analyses (i.e. inter-speaker variation in analyses). In a production task with the -ity suffix and nonce stems, Pierrehumbert (2006) found that 2 in 10 participants never applied velar softening. This suggests that approximately 20% of learners may not learn the grammar for velar softening, but may instead memorize full underlying forms (e.g. /ɪlɛktɹɪsɪti/) for existing words. For velar softening, my model not only correctly predicts that there are multiple solutions for one surface pattern, it also correctly predicts the proportion of human speakers that will pick each solution.In the Rich Base problem, there are two grammars that satisfy one surface pattern. However, humans only acquire one grammar - the Rich Base Grammar (as evidenced by loan word adaptation). In my simulated population of language learners, I find an overwhelming preference for the Rich Base Grammar to be learned. This preference emerges from my model's ability to leverage the superior utility of the Rich Base Grammar over its non-Rich Base counterpart (without needing to build in any extra mechanisms or biases).English CiV lengthening appears at first blush to be a derived environment effect, whose triggering condition - the derived environment - cannot be directly observed. My experiments confirm the productivity of CiV lengthening. I reanalyze CiV lengthening as the emergence of the unmarked Stress-to-Weight Principle, thus simplifying CiV Lengthening from a complex hidden structure problem to a surface true phenomenon.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Sociolinguistics. $3 555401
650 4 $a Language. $3 571568
650 4 $a Linguistics. $3 557829
653 $a Emergence of the unmarked
653 $a Hidden structure
653 $a Maximum entropy
653 $a Phonology
653 $a Underlying representation
655 7 $a Electronic books. $2 local $3 554714
690 $a 0290
690 $a 0679
690 $a 0636
710 2 $a University of California, Los Angeles. $b Linguistics 0510. $3 1183936
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 85-09A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31140695 $z click for full text (PQDT)