| dc.contributor.author | Luef, Eva Maria |
| dc.date.accessioned | 2022-09-16T14:55:55Z |
| dc.date.available | 2022-09-16T14:55:55Z |
| dc.date.issued | 2022 |
| dc.identifier.uri | http://hdl.handle.net/11234/1-4793 |
| dc.description | Phonological networks are representations of word forms and their phonological relationships with other words in a given language lexicon. A principle underlying the growth (or evolution) of those networks is preferential attachment, or the ‘rich-gets-richer’ mechanisms, according to which words with many phonological neighbors (or links) are the main beneficiaries of future growth opportunities. Due to their limited number of words, language lexica constitute node-constrained networks where growth cannot keep increasing in a linear way; hence, preferential attachment is likely mitigated by certain factors. The present study investigated aging effects (i.e., a word’s finite time span of being active in terms of growth) in an evolving phonological network of English as a second language. It was found that phonological neighborhoods are constructed by one large initial lexical spurt, followed by sublinear growth spurts that eventually lead to very limited growth in later lexical spurts during network evolution, all the while obeying the law of preferential attachment. An analysis of the strength of phonological relationships between phonological word forms revealed a tendency to attach more distant phonological neighbors in the lower proficiency levels, while phonologically more similar neighbors enter phonological neighborhoods at more advanced levels of English as a second language. Overall, the findings suggest an aging effect in growth that favors younger words. In addition, beginning learners seem to prefer the acquisition of phonological neighbors that are easier to discriminate. Implications for the second language lexicon include leveraged learning mechanisms, learning bouts focussed on a smaller range of phonological segments, and involve questions concerning lexical processing in aging networks. |
| dc.language.iso | eng |
| dc.publisher | Charles University |
| dc.rights | Creative Commons - Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0) |
| dc.rights.uri | http://creativecommons.org/licenses/by-nd/4.0/ |
| dc.subject | network aging |
| dc.subject | English as a second language |
| dc.subject | network evolution |
| dc.subject | phonological network |
| dc.subject | preferential attachment |
| dc.title | Aging effects in an evolving phonological network |
| dc.type | corpus |
| metashare.ResourceInfo#ContentInfo.mediaType | text |
| dc.rights.label | PUB |
| has.files | yes |
| branding | LINDAT / CLARIAH-CZ |
| contact.person | Eva Maria Luef evamaria.luef@ff.cuni.cz Charles University, Faculty of Arts, Department of English Language and ELT Methodology |
| size.info | 1 files |
| files.size | 148794 |
| files.count | 2 |
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- Název
- Data_Aging_phon_network.xlsx
- Velikost
- 143.94 KB
- Formát
- Microsoft Excel 2007
- Popis
- data
- MD5
- b9ca669a3d9925b26d4a84045ad08f7e
- Název
- README.txt
- Velikost
- 1.36 KB
- Formát
- Textový soubor
- Popis
- data format description
- MD5
- 8eda5f701cf7c1363eb1802287074307
The Excel sheet contains data for each of the investigated proficiency levels
of L2 English (i.e., A1, A2, B1, B2, C1). Each separate profiiency data sheet
is set up the same way:
1. the first column contains the words that are added to a lexicon at the
specific proficiency level. At the A1 level, these words undergo the maximal
number of growth spurts as they age in the lexicon. At the C1 level, the newly
learned words only undergo one growth spurt (to C2).
2. Next, the raw number of phonological neighbors that exist within a given
proficiency level are listed.
3. The proportions of known words at a given proficiency level are calculated
in relation to the maximal number of neighbors known at the last L2 proficiency
level C2. So, C2 words/ neighbors represent 100%.
4. The average weighted degrees of words at the different proficiency levels
are listed in the next few columns.
5. The Clearpond-ENGLISH data base was used to determine the L1-given maximal
neighbo . . .