Comparative Analysis of Networks of Phonologically Similar Words in English and Spanish

This is the publisher's version, also available electronically from http://www.mdpi.com/1099-4300/12/3/327.Previous network analyses of several languages revealed a unique set of structural characteristics. One of these characteristics—the presence of many smaller components (referred to as islands)—was further examined with a comparative analysis of the island constituents. The results showed that Spanish words in the islands tended to be phonologically and semantically similar to each other, but English words in the islands tended only to be phonologically similar to each other. The results of this analysis yielded hypotheses about language processing that can be tested with psycholinguistic experiments, and offer insight into cross-language differences in processing that have been previously observed

What do foreign neighbors say about the mental lexicon?

A corpus analysis of phonological word-forms shows that English words have few phonological neighbors that are Spanish words. Concomitantly, Spanish words have few phonological neighbors that are English words. These observations appear to undermine certain accounts of bilingual language processing, and have significant implications for the processing and representation of word-forms in bilinguals.This research was supported in part by a grant from the National Institutes of Health to the University of Kansas through the Schiefelbusch Institute for Life Span Studies: National Institute on Deafness and Other Communication Disorders R01 DC 006472

Community structure in the phonological network

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author’s publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Community structure, which refers to the presence of densely connected groups within a larger network, is a common feature of several real-world networks from a variety of domains such as the human brain, social networks of hunter-gatherers and business organizations, and the World Wide Web (Porter et al., 2009). Using a community detection technique known as the Louvain optimization method, 17 communities were extracted from the giant component of the phonological network described in Vitevitch (2008). Additional analyses comparing the lexical and phonological characteristics of words in these communities against words in randomly generated communities revealed several novel discoveries. Larger communities tend to consist of short, frequent words of high degree and low age of acquisition ratings, and smaller communities tend to consist of longer, less frequent words of low degree and high age of acquisition ratings. Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities. Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935). The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language

Spoken word recognition and serial recall of words from the giant component and words from lexical islands in the phonological network

Network science is a field that applies mathematical techniques to study complex systems, and the tools of network science have been used to analyze the phonological network of language (Vitevitch, 2008). The phonological network consists of a giant component, lexical islands, and several hermits. The giant component represents the largest connected component of the network, whereas lexical islands constitute smaller groups of words that are connected to each other but not to the giant component. To determine if the size of the network component that a word resided in influenced lexical processing, three psycholinguistic tasks (word shadowing, lexical decision, and serial recall) were used to compare the processing of words from the giant component and word from lexical islands. Results showed that words from lexical islands were more quickly recognized and more accurately recalled than words from the giant component. These findings can be accounted for via a spreading activation framework. Implications for models of spoken word recognition and network science are also discussed

Comparison of the language networks from literature and blogs

In this paper we present the comparison of the linguistic networks from literature and blog texts. The linguistic networks are constructed from texts as directed and weighted co-occurrence networks of words. Words are nodes and links are established between two nodes if they are directly co-occurring within the sentence. The comparison of the networks structure is performed at global level (network) in terms of: average node degree, average shortest path length, diameter, clustering coefficient, density and number of components. Furthermore, we perform analysis on the local level (node) by comparing the rank plots of in and out degree, strength and selectivity. The selectivity-based results point out that there are differences between the structure of the networks constructed from literature and blogs

Quantitative learning strategies based on word networks

Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network

Ab Antiquo: Neural Proto-language Reconstruction

Historical linguists have identified regularities in the process of historic sound change. The comparative method utilizes those regularities to reconstruct proto-words based on observed forms in daughter languages. Can this process be efficiently automated? We address the task of proto-word reconstruction, in which the model is exposed to cognates in contemporary daughter languages, and has to predict the proto word in the ancestor language. We provide a novel dataset for this task, encompassing over 8,000 comparative entries, and show that neural sequence models outperform conventional methods applied to this task so far. Error analysis reveals variability in the ability of neural model to capture different phonological changes, correlating with the complexity of the changes. Analysis of learned embeddings reveals the models learn phonologically meaningful generalizations, corresponding to well-attested phonological shifts documented by historical linguistics.Comment: Accepted as a long paper in NAACL2

The Structure of Phonological Networks Across Multiple Languages

The network characteristics based on the phonological similarities in the lexicons of several languages were examined. These languages differed widely in their history and linguistic structure, but commonalities in the network characteristics were observed. These networks were also found to be different from other networks studied in the literature. The properties of these networks suggest explanations for various aspects of linguistic processing and hint at deeper organization within human language.Comment: 5 pages, 3 figures, 2 tables, submitted to Phys. Rev.

Rhythm in late-modern Stockholm: Social stratification and stylistic variation in the speech of men

PhD thesisThe main finding of this thesis is that rhythm is a stratified variable in the speech of men in Stockholm. An epicenter for the social forces associated with late modernity, Stockholm is also home to Europe’s ‘first’ multiethnolect (Rinkeby Swedish, Kotsinas 1988a). Swedish-language researchers describe the variety as ‘staccato’, but rhythm has not been thoroughly investigated for any variety of Stockholm Swedish in production. Data come from 36 male Stockholmers, ages 24–45, from a stratified sample of social classes. Seventeen self-identify by the term svensk (Swedish) and 19 by the highly racialized term invandrare (literal translation: immigrant). All were born in Sweden save for three who arrived before age seven. Three contextual styles were elicited to capture a speech-formality cline: CASUAL, READING, and RADIO (reading like a radio announcer). Rhythm is operationalized with an adaptation of the nPVIV algorithm (Low, Grabe, & Nolan 2000). Not only does rhythm stratify predictably in the direction of staccato (low alternation) for the racialized working class, it also is significantly high-alternation/non-staccato in the speech of the white working class. The former is interpreted to be a feature of multiethnolect; the latter a feature of Södersnack, Stockholm’s industrial-era working-class variety. The higher classes produce an intermediate degree of rhythm in their casual speech. Rhythm variation among the working classes is also stylistically sensitive. Working-class READING and RADIO appear to target upper-class CASUAL. The racialized working class shows a stylistic sensitivity that is stronger among younger speakers than old, implying a transition from indicator to marker (Labov 1972a:179) for staccato rhythm. The white working class shows a high degree of stylistic sensitivity regardless of age, implying that high alternation is a Södersnack legacy feature. Generational differences in rhythm production are examined within the racialized working class, and a change point is identified between those born before 1983 and those after. Those born before 1983 mainly achieve ‘staccato’ with a reduction of accented phonologically-long vowels. Those born after 1983 achieve it with an innovation; they enlarge unstressed vowels, both phonologically short and long. ‘Reduction’ and ‘enlargement’ refer to duration, f0, and energy. The change point coincides with historical spikes in migration, inequality, and school segregation that would have occurred when the speakers were in adolescence. In all contextual styles, age is a stable predictor of rhythm, independent of social class and racialization. Younger speakers of any background have more staccato speech than older speakers of the same background. It is proposed that this is due to the diffusion of contact prosody, for which multiethnolect is one key conduit

Phonological similarity influences word learning in adults learning Spanish as a foreign language

Neighborhood density—the number of words that sound similar to a given word (Luce & Pisoni, 1998)—influences word-learning in native English speaking children and adults (Storkel, 2004; Storkel, Armbruster, & Hogan, 2006): novel words with many similar sounding English words (i.e., dense neighborhood) are learned more quickly than novel words with few similar sounding English words (i.e., sparse neighborhood). The present study examined how neighborhood density influences word-learning in native English speaking adults learning Spanish as a foreign language. Students in their third-semester of Spanish language classes learned advanced Spanish words that sounded similar to many known Spanish words (i.e., dense neighborhood) or sounded similar to few known Spanish words (i.e., sparse neighborhood). In three word-learning tasks, performance was better for Spanish words with dense rather than sparse neighborhoods. These results suggest that a similar mechanism may be used to learn new words in a native and a foreign language