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In this talk, I report on joint work with Jan Strunk and Balthasar Bickel

The probability for function words to develop into affixes differs for different functions, e.g. the preference for suffixing as opposed to prefixing is more pronounced for case than for person markers (Cysouw 2009). A possible explanation links this difference to the extent to which markers predict a specific construction (Himmelmann 2014). Here, we show that in addition to this function-specific effect, prefixation probabilities differ globally between nominal and verbal hosts, and we suggest that this difference is grounded in different probabilities of speech pauses before these hosts. No parallel effects are found after hosts.

To estimate affixation probabilities, we extracted information on fusion together with information on position relative to the phonological host (noun vs. verb) for a wide range of markers in the AUTOTYP database (Bickel et al. 2017). This includes systematic samples of case, agreement, tense, number, and negation markers and less systematic samples of many other categories collected in various projects with a total of well over 2,000 markers studied. Results shown that the chance for fused (vs. non-fused) markers is significantly lower before nominal than before verbal hosts. No such effect was observed with postposed markers, i.e. while the host category has an effect on prefixation, it has no such effect on suffixation

An explanation for this comes from measurements of pause probabilities before nouns vs. before verbs in corpora documenting spontaneous speech of nine languages (between 17.500 and 37.550 words each) (Seifart et al. 2018): Baure (Arawakan), Bora (Boran), Chintang (Sino-Tibetan), Even (Tungusic), Hoocak (Siouan), N|uu (Tuu), Texistepec (Mixe-Zoquean), Dutch and English (Germanic). In these data, the likelihood for pauses before nouns is significantly higher than before verbs We attribute this finding to the fact that lexical nouns are usually appropriate pragmatically only for new referents (vs. pronoun ­or gap; Levinson 2000), a constraint which induces increased time for lexical access (Levelt, Roelofs & Meyer 1999). This contrasts with verbs, where there are no such choices and lexical access time does not seem to depend on information structure.

This suggests that the increased pause probabilities before nouns vs. before verbs inhibits the phonological fusion of pre-posed functional elements before nouns, but not before verbs, and thus explains the asymmetry in the occurrence of prefixes across parts of speech in the languages of the world.

References:

Bickel, Balthasar, Johanna Nichols, Taras Zakharko, Alena Witzlack-Makarevich, Kristine Hildebrandt, Michael Rießler, Lennard Bierkandt, Fernando Zúñiga & John B. Lowe. 2017. /The AUTOTYP typological databases, version 0.1.0/. https://github.com/autotyp/autotyp-data/tree/0.1.0.

Bickel, Balthasar. 2013. Distributional biases in language families. In Bickel, Balthasar, Lenore A. Grenoble, David A. Peterson & Alan Timberlake (eds.), /Language typology and historical contingency/, 415–444. Amsterdam: Benjamins.

Cysouw, Michael. 2009. The asymmetry of affixation. /Snippets/ 20(3). 10–14.

Himmelmann, Nikolaus P. 2014. Asymmetries in the prosodic phrasing of function words: Another look at the suffixing preference. /Language/ 90(4). 927–960.

Levelt, Willem J. M., Ardi Roelofs & Antje S. Meyer. 1999. A theory of lexical access in speech production. /Behavioral and Brain Sciences/ 22(01). 1–38.

Levinson, Stephen C. 2000. /Presumptive meanings. The theory of generalized conversational implicature/. Cambridge, MA: MIT Press.

Seifart, Frank, Jan Strunk, Swintha Danielsen, Iren Hartmann, Brigitte Pakendorf, Søren Wichmann, Alena Witzlack-Makarevich, Nivja H. de Jong & Balthasar Bickel. 2018. Nouns slow down speech across structurally and culturally diverse languages. /Proceedings of the National Academy of Sciences/ 115(22). 5720–5725. doi: 10.1073/pnas.1800708115 .

Zakharko, Taras & Balthasar Bickel. 2011ff. familybias: Family bias estimation. R package, https://github.com/IVS-UZH.

• Denis Paperno (LORIA)

We propose creating questionnaires for lexical typology (in the spirit of Rakhilina and Reznikova 2016) in an automatized fashion. We evaluate our system on questionnaire creation for 'smooth', 'sharp', 'thick', and 'straight' (object features often but not always expressed by adjectives), and perform a quantitative and qualitative analysis of the results. Our algorithm consists of the following steps: 1) extracting a list of frequent phrases, or bigrams, of the form “adjective + noun”; 2) computing a co-occurrence-based vector representation for every noun phrase; 3) clustering the vector space; 4) extracting three core elements from the each cluster while eliminating all clusters containing less than three elements. This algorithm allows revealing semantic oppositions that indeed are typologically relevant. For example, many languages distinguish lexically ‘sharp edges (e.g. knives)’ and ‘sharp points (e.g. arrows)’, having two distinct adjectives with the meaning ‘sharp’: one for the first sense, another for the second one (compare tranchant/aiguisé vs. pointu in French). There is no such distinction in Russian; still, Russsian noun phrases illustrating these context types fall into two different clusters (ostryj nož ‘sharp knife’, ostryj nožik ‘sharp little knife’, ostroje lezvije ‘sharp blade’ vs. ostraja strela ‘sharp arrow’, ostroje kop’ë ‘sharp spear’, ostryj kamen’ ‘sharp stone’).