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Constructional (n (no) ) synonymy: a usage-based analysis of f Comple lete Path in in Polis lish Daria Bbeniec and Magorzata Cudna Maria Curie- Skodowska University, Lublin Linguistics Beyond and Within, 22-23 October 2015 Two ne

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SLIDE 1

Constructional (n (no) ) synonymy: a usage-based analysis of f Comple lete Path in in Polis lish

Daria Bębeniec and Małgorzata Cudna Maria Curie-Skłodowska University, Lublin Linguistics Beyond and Within, 22-23 October 2015

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SLIDE 2

Two ne near-synonymous Pol

  • lis

ish con

  • nstr

tructio ions exp xpressin ing the the con

  • ncept of
  • f Com
  • mple

lete Path th

  • od ______________ do ______________
  • od ______________ po ______________

NP (GEN) NP (GEN) NP (ACC) NP (GEN)

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SLIDE 3

Ex Exam ample les of

  • f use

use

  • 1) (…) rzeczą kardynalną jest przestrzeganie każdego prawa, od

najmniejszego do najważniejszego. [NKJP]

  • ‘(…) it is fundamental that every law be obeyed, from the smallest to

the most important.’

  • 2) Pogłoski (…) skusiły ludzi w różnym wieku – od licealistów po

emerytów. [NKJP]

  • ‘The rumours (…) tempted people of different ages – from high school

students to old age pensioners.’

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SLIDE 4

Com

  • mplete Path an

and its its ins instances in in Pol

  • lis

ish

Complete Path cx Form: source-PP goal-PP Meaning: COMPLETE PATH

  • d-do
  • d-po
  • d-ku

z-do z-na …

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SLIDE 5

Con

  • nstructio

ions

  • “basic units of language” (Goldberg 1995: 4)
  • “conventional,

learned form-function pairings at varying levels

  • f

complexity and abstraction” (Goldberg 2013: 17)

  • Non-compositional meaning
  • Unpredictable (constrained) form
  • Sufficient frequency of occurrence
  • Special collocational preferences

Goldberg 1995 Goldberg 2006 Gries et al. 2005, Hilpert 2008, 2014

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SLIDE 6

Con

  • nstructio

ions ct ctd.

  • Constructions are connected via several kinds of inheritance links,

including instance links, polysemy links, metaphorical extension links and subpart links (Goldberg 1995: 72-80)

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SLIDE 7

CP

  • d-do
  • d-po
  • Generalizations are formed on

the basis of both form and meaning/function (e.g., Boyd and Goldberg 2011)

  • Do we have this generalization
  • ver the specific constructions?
  • Principle of No Synonymy

(Goldberg 1995: 67)

  • What usage patterns are

associated with each of the two constructions?

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SLIDE 8

Today’s foc

  • cus:

: len length of

  • f prepositional complements

(LM (LM-phrases)

  • Length is one of possible operationalizations of complexity
  • the dative alternation (Bresnan 2007, Bresnan et al. 2007, Bresnan

and Ford 2010, Wolk et al. 2013)

  • the genitive alternation (Rosenbach 2003, Hinrichs and Szmrecsanyi

2007, Wolk et al. 2013, Ehret et al. 2014)

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SLIDE 9

Our ur pr predic ictions/expectatio ions

  • od-do is going to be associated with shorter LMs than od-po and its LMs

are going to be elaborated by phrases at the same level of specificity and hence complexity (cf. Przybylska 2002: 483-487 on the differences in meaning between the prepositions do and po) → explanation based on both iconicity and frequency

  • EXAMPLES:
  • … od okrutnego strachu do bezrównej odwagi ‘from terrible fear to

unequalled courage’ [NKJP]

  • … od jesieni 1920 do jesieni 1921 ‘from autumn 1920 to autumn 1921’

[NKJP]

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SLIDE 10

Our ur pr predic ictions/expectatio ions

  • in both constructions LM1 is going to be shorter than LM2 (due to

processing constraints, cf. Wasow 1997 on the principle of end- weight)

  • EXAMPLE:
  • … od najjaśniejszych po najciemniejsze strony życia ‘from the

brightest to the darkest sides of life’ [NKJP]

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SLIDE 11

Data a an and meth thod

  • 529 instances of both constructions (od-do: 291, od-po: 238)
  • 300m balanced subsection of NKJP (Przepiórkowski et al. 2012)
  • POLIQARP NKJP (Janus and Przepiórkowski 2007)
  • usage-feature approach (e.g., Glynn 2009, 2010) / behavioural-profile

approach (e.g., Divjak and Gries 2006, Gries and Divjak 2009)

  • R statistical environment 2.15.2 (R Development Core Team 2008)
  • Univariate

and multivariate methods: chi-square test, multiple correspondence analysis, cluster analysis, logistic regression

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Co Comparing th the tw two con

  • nstructions
  • LM1 – syllables
  • LM2 – syllables
  • LM1 – words
  • LM2 – words
  • Difference between LMs – syllables
  • Strict difference between LMs – syllables
  • Difference between LMs – words
  • Strict difference between LMs – words

Szmrecsanyi 2004 Wolk et al. 2013

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SLIDE 13

Cod

  • din

ing sch schema

VARIABLE LEVELS COMMENTS LM1.Length.levels.syllables short, medium, long 1-3 syl, 4-8 syl, 9 syl and more LM2.Length.levels.syllables short, medium, long 1-3 syl, 4-8 syl, 9 syl and more LM1.Length.levels.words short, medium, long 1 word, 2-3 words, 4 words and more LM2.Length.levels.words short, medium, long 1 word, 2-3 words, 4 words and more LMs.Length.difference.syllables LM1 longer, LM2 longer, same length +/-2 syl LMs.Length.strict.difference.syllables LM1 longer, LM2 longer, same length LMs.Length.difference.words LM1 longer, LM2 longer, same length +/-1 word LMs.Length.strict.difference.words LM1 longer, LM2 longer, same length

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the catdes function from the FactoMineR package

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Categ egory ry des descriptio ion

OD-DO OD-PO

  • 1. LM1 in syllables and words: long
  • 2. LM2 in words: medium
  • 3. LM2 in syllables: long
  • 4. Strict difference between LMs in

words: LM1 longer

  • 1. LM2 in words: short
  • 2. LM2 in syllables: short and medium
  • 3. Difference between LMs in syllables:

same length

  • 4. Difference between LMs in words:

LM2 longer This is contrary to our expectations! New explanation needed.

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SLIDE 16

Tak akin ing bo both constructio ions tog

  • gether
  • LM1-LM2 – syllables
  • LM1-LM2 – words
  • LM1 – syllables
  • LM2 – syllables
  • LM1 – words
  • LM2 – words
  • Difference between LMs – syllables
  • Strict difference between LMs – syllables
  • Difference between LMs – words
  • Strict difference between LMs – words

Chi-square test for given probabilities Chi-square test for independence

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SLIDE 17

Chi hi-square tes ests for independence (LM (LM1, LM2 in n the the whole le sam sample)

VARIABLE p-value COMMENTS LM1.Length.levels.syllables LM2.Length.levels.syllables 2.136e-33

√

LM1.Length.levels.words LM2.Length.levels.words 3.952e-34

√

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SLIDE 18

Chi hi-square tes ests for given pr probabil ilit itie ies (th (the e who hole sam sample)

VARIABLE p-value COMMENTS LM1.Length.levels.syllables 0.0003722

√

LM2.Length.levels.syllables 1.225e-06

√

LM1.Length.levels.words 9.993e-10

√

LM2.Length.levels.words 4.674e-11

√

LMs.Length.difference.syllables 2.2e-16

√

LMs.Length.strict.difference.syllables 4.32e-15

√

LMs.Length.difference.words 2.2e-16

√

LMs.Length.strict.difference.words 8.054e-09

√

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SLIDE 19

Th The ten endencies con

  • nfir

irmed

  • 1. LM1/LM2 in words and syllables: in most cases LM1 short = LM2

short, LM1 medium = LM2 medium, LM1 long = LM2 long

  • 2. LM1 in words and syllables: short and medium in most cases
  • 3. LM2 in words and syllables: medium and medium in most cases
  • 4. Difference between LMs in words and syllables: same length in

most cases

  • 5. Strict difference between LMs in words and syllables: LM2 longer in

most cases Our expectations are borne out this time.

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SLIDE 20

Con

  • nclusions
  • We have shown that even with a small set of complexity-related

variables it is possible to identify some usage patterns associated with both constructions under analysis

  • We have found some usage-based evidence for the existence of the

general constructional schema (CP)

  • We have compared two operationalizations of complexity (cf.

Szmrecsanyi 2004)

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SLIDE 21
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SLIDE 22

TH THANK YOU!

Daria Bębeniec

daria@hektor.umcs.lublin.pl

Małgorzata Cudna

mcudna@hektor.umcs.lublin.pl

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SLIDE 23

References (1 (1)

  • Boyd Jeremy K. and Adele E. Goldberg. 2011. “Learning what not to say: The role of statistical

preemption and categorization in a-adjective production”, Language 87 (1): 55-83.

  • Bresnan Joan. 2007. “Is syntactic knowledge probabilistic? Experiments with the English dative

alternation”, in S. Featherston and W. Sternefeld (eds.), Roots: Linguistics in Search of Its Evidential Base, Berlin: Mouton de Gruyter, pp. 77-96.

  • Bresnan Joan, Anna Cueni, Tatiana Nikitina and R. Harald Baayen. 2007. “Predicting the Dative

Alternation”, in G. Boume, I. Kraemer and J. Zwarts (eds.), Cognitive Foundations of Interpretation, Amsterdam: Royal Netherlands Academy of Science, pp. 69-94.

  • Bresnan Joan and Marilyn Ford. 2010. “Predicting syntax: Processing dative constructions in

American and Australian varieties of English”, Language 86 (1): 168–213.

  • Divjak Dagmar and Stefan Th. Gries. 2006. “Ways of trying in Russian: Clustering behavioural

profiles.” Corpus Linguistics and Linguistic Theory 2, 3-60.

  • Ehret Katharina, Christoph Wolk and Benedikt Szmrecsanyi. 2014. “Quirky quadratures: on

rhythm and weight as constraints on genitive variation in an unconventional data set”, English Language and Linguistics 18 (2): 263-303.

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SLIDE 24

References (2 (2)

  • Glynn Dylan. 2009. “Polysemy, syntax, and variation: A usage-based method for Cognitive

Semantics”, in V. Evans and S. Pourcel (eds.) New Directions in Cognitive Linguistics, Amsterdam and Philadelphia: John Benjamins, pp. 77-104.

  • Glynn Dylan. 2010. “Testing the hypothesis: Objectivity and verification in usage-based Cognitive

Semantics”, in D. Glynn and K. Fischer (eds.) Quantitative Methods in Cognitive Semantics: Corpus- Driven Approaches, Berlin and New York: Mouton de Gruyter, pp. 239-270.

  • Goldberg Adele E. 1995. Constructions: A Construction Grammar Approach to Argument
  • Structure. Chicago and London: University of Chicago Press.
  • Goldberg Adele E.
  • 2006. Constructions at Work: The Nature of Generalization in Language.

Oxford: Oxford University Press.

  • Goldberg Adele E. 2013. “Constructionist approaches”, in Th. Hoffmann and G. Trousdale (eds.),

The Oxford Handbook of Construction Grammar, Oxford: Oxford University Press, pp. 15-31.

  • Gries Stefan Th., Beate Hampe and Doris Schönefeld. 2005. “Converging evidence: Bringing

together experimental and corpus data on the association of verbs and constructions”, Cognitive Linguistics 16 (4): 635-676.

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SLIDE 25

References (3 (3)

  • Gries Stefan Th. and Dagmar Divjak. 2009. “Behavioral profiles: a corpus-based approaches

towards cognitive semantic analysis.”, in V. Evans and S. Pourcel (eds.) New Directions in Cognitive Linguistics, Amsterdam & Philadelphia: John Benjamins, pp. 57-75.

  • Hilpert Martin. 2008. Germanic Future Constructions: A Usage-Based Approach to Language
  • Change. Amsterdam and Philadelphia: John Benjamins.
  • Hilpert Martin. 2014. Construction Grammar and its Application to English. Edinburgh: Edinburgh

University Press.

  • Hinrichs Lars and Benedikt Szmrecsanyi. 2007. “Recent changes in the function and frequency of

Standard English genitive constructions: a multivariate analysis of tagged corpora”, English Language and Linguistics 11 (3): 437–474.

  • Janus Daniel and Adam Przepiórkowski. 2007. “Poliqarp 1.0: Some technical aspects of a linguistic

search engine for large corpora,” in: J. Waliński, K. Kredens and S. Góźdź-Roszkowski (eds.) The proceedings of Practical Applications in Language and Computers PALC 2005. Frankfurt am Mein: Peter Lang.

  • Przepiórkowski Adam, Mirosław Bańko, Rafał L. Górski and Barbara Lewandowska-Tomaszczyk

(2012). Narodowy Korpus Języka Polskiego. Warszawa: Wydawnictwo Naukowe PWN.

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SLIDE 26

References (4 (4)

  • Przybylska Renata. 2002. Polisemia przyimków polskich w świetle semantyki kognitywnej. Kraków:

Universitas.

  • R Development Core Team (2008). R: A language and environment for statistical computing. R

Foundation for Statistical Computing, Vienna, Austria.

  • Rosenbach Anette. 2003. “Aspects of iconicity and economy in the choice between the s-genitive

and the of genitive in English”, in G. Rohdenburg and B. Mondorf (eds.), Determinants of Grammatical Variation in English, Berlin: Mouton de Gruyter, pp. 379–411.

  • Szmrecsanyi Benedikt. 2004. “On Operationalizing Syntactic Complexity”, in G. Purnelle, C. Fairon,

abd A. Dister (eds.) Le poids des mots. Proceedings of the 7th International Conference on Textual Data Statistical Analysis Vol. 2, Louvain-la-Neuve: Presses universitaires de Louvain, pp. 1032- 1039.

  • Wasow Thomas. 1997. “Remarks on grammatical weight”, Language Variation and Change 9 (1):

81-105.

  • Wolk Christoph, Joan Bresnan, Anette Rosenbach and Benedikt Szmrecsanyi. 2013. “Dative and

genitive variability in Late Modern English. Exploring cross-constructional variation and change”, Diachronica 30 (3): 382–419.

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SLIDE 27

Additional sli lides

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SLIDE 30
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SLIDE 32

Rela elativ ive fr frequencie ies of

  • f con
  • nstructio

ional mea eanin ings (coarse-grain ined)

20% 39% 41%

OD-DO

SPATIAL TEMPORAL OTHER (ABSTRACT)

35% 12% 53%

OD-PO

SPATIAL TEMPORAL OTHER (ABSTRACT)

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SLIDE 33

Chi hi-square tes ests for independence (Len ength, , Con

  • nstructio

ion.Type)

VARIABLE p-value COMMENTS LM1.Length.levels.syllables 0.0006004

√

LM2.Length.levels.syllables 0.0269

√

LM1.Length.levels.words 0.001404

√

LM2.Length.levels.words 0.003199

√

LMs.Length.difference.syllables 0.05956

  • LMs.Length.strict.difference.syllables

0.4569

  • LMs.Length.difference.words

0.02623

√

LMs.Length.strict.difference.words 0.06582

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SLIDE 34

Th The sam sample le

  • 300m balanced subsection of NKJP (Przepiórkowski et al. 2012)
  • POLIQARP NKJP (Janus and Przepiórkowski 2007)
  • 529 instances of both cxs
  • [orth=od/i & pos=prep & case=gen] [pos!=interp] {1} [orth=do/i &

pos=prep & case=gen]

  • [orth=od/i & pos=prep & case=gen] [pos!=interp] {1} [orth=po/i &

pos=prep & case=acc]

  • And then {2}, {3} etc.
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SLIDE 35

OD-DO OD-PO Distance between Prep1 and Prep2 in words All instances taken from the corpus Constructional instances All instances taken from the corpus Constructional instances 1 100 (out of 51292) 96 100 (out of 121) 90 2 100 (out of 16432) 79 85 (out of 85) 73 3 100 (out of 7895) 32 45 (out of 45) 29 4 100 (out of 5627) 24 38 (out of 38) 23 5 100 (out of 3966) 25 24 (out of 24) 8 6 100 (out of 3010) 9 16 (out of 16) 5 7 100 (out of 2254) 11 12 (out of 12) 5 8 100 (out of 1725) 6 8 (out of 8) 3 9 100 (out of 1317) 8 5 (out of 5) 10 100 (out of 981) 1 6 (out of 6) 2 291 238

Total: 529

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SLIDE 36

All ll vs.

  • s. con
  • nstructio

ional

Cx All Constructional

OD-DO 1000 291 (29%) OD-PO 339 238 (70%)

If we wanted to analyse all the tokens from the corpus, then:

OD-DO 94499 ≈28000 OD-PO 360 ≈250

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SLIDE 37

Th The whole le codin ing sch schema

Variable Levels Construction.Type

  • d-do, od-po

LM1.Number LM1 Number Sg, LM1 Number Pl, n/a LM2.Number LM2 Number Sg, LM2 Number Pl, n/a LMs.Number.difference LMs Number Diff, LMs Number Same, n/a LMs.Length.difference.words LM1 Longer, LM2 Longer, LMs Same Length LMs.Semantic.category Places Points, Places Ext, Places Parts, Time Units, Abst Bounds, Abst Bounds Quant LMs.Syntactic.function.coarse Modifer, Adverbial or Oth TR.Number TR Number Sg, TR Number Pl, TR Number NA Particle.aż Present, Absent Medial.path.przez.poprzez Present, Absent

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SLIDE 38

Cod

  • din

ing sch schema ct ctd.

Variable Levels TR.Expression Overt, Covert TR.Animacy Animate, Inanimate, n/a LMs.Syntactic.complexity LM1 More Complex, LM2 More Complex, Same Complex Clause.Type Main, Subordinate LM1.Animacy Animate, Inanimate LM2.Animacy Animate, Inanimate LMs.Syntactic.function Modifer, Adverbial, Prepositional Complement, Sentence Fragment Construction.Use.type Serial, NonSerial Construction.Position Initial, Middle, Final Text.type.coarse Spoken, Written

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SLIDE 39

Cod

  • din

ing sch schema ct ctd.

Variable Levels Meaning.coarse Space, Time, Metaphorical or Other Meaning.very coarse Abstract, Spatial LM1.Length.levels.syllables Short, Medium, Long LM2.Length.levels.syllables Short, Medium, Long LM1.Length.levels.words Short, Medium, Long LM2.Length.levels.words Short, Medium, Long LMs.Length.difference.syllables LM1 Longer, LM2 Longer, Same Length LMs.Length.strict.difference.syllables LM1 Longer, LM2 Longer, Same Length LMs.Length.strict.difference.words LM1 Longer, LM2 Longer, Same Length Structural similarity (for Adj, Prep, Conj, Ger, Part, Num) Structural.similarity, No.Str.Similarity

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