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Fig. 1 Frequency distribution of neighborhood density measurements. Note that the axes are not the same definition. The first (Maximal Neighborhood Density, column AJ) defines neighbors as signs that share any four of the five sub-lexical properties described above. Because these five sub-lexical properties offered in ASL-LEX do not uniquely identify each sign, the neighborhood density definitions of- fered here differ from the traditional definitions of neighbor- hood density used for spoken language in that here neighbors are not necessarily true minimal pairs. The distribution for Maximal Neighborhood Density values was extremely skewed toward fewer neighbors (Mdn = 27; see Fig. 1A). The distribution of spoken English neighborhood density in the English Lexicon Project (Balota et al., 2007) is also skewed toward fewer neighbors (Min = 0, ha = 48, Mdn = 0). Signed languages are thought to have unusually small numbers of neighbors relative to spoken languages (true minimal pairs are extremely rare; van der Kooij, 2002), so Maximal Neighborhood Density may not fully capture the phonological structure of the lexicon. For this reason, an ad- ditional neighborhood density measure (Minimal eighborhood Density, column AJ) was added that defines neighbors as signs that overlap in at least one feature of any kind with the target. The median Minimal Neighborhood Density is 780 (see Fig. 1B). Because Minimal Neighoborhood Density includes quite distant neighbors, the distribution is skewed toward more neighbors. There is also a The neighborhood density measures described above calcu- ate the number of shared sub-lexical properties irrespective of he type of property (i.e., location, movement, handshape). However, much of the linguistic work on sign languages has focused on the relationship between signs that share a partic- ular sub-lexical feature (e.g., location) and the “neighborhood density” for that sub-lexical feature (e.g., location neighbor- hood density, handshape neighborhood density; Baus, Gutiérrez-Sigut, Quer, & Carrieras, 2008; Baus, Gutiérrez & Carreiras, 2014; Corina & Emmorey, 1993; Corina & Hildebrandt, 2002; Corina & Knapp, 2006; Dye & Shih,

Figure 1 Frequency distribution of neighborhood density measurements. Note that the axes are not the same definition. The first (Maximal Neighborhood Density, column AJ) defines neighbors as signs that share any four of the five sub-lexical properties described above. Because these five sub-lexical properties offered in ASL-LEX do not uniquely identify each sign, the neighborhood density definitions of- fered here differ from the traditional definitions of neighbor- hood density used for spoken language in that here neighbors are not necessarily true minimal pairs. The distribution for Maximal Neighborhood Density values was extremely skewed toward fewer neighbors (Mdn = 27; see Fig. 1A). The distribution of spoken English neighborhood density in the English Lexicon Project (Balota et al., 2007) is also skewed toward fewer neighbors (Min = 0, ha = 48, Mdn = 0). Signed languages are thought to have unusually small numbers of neighbors relative to spoken languages (true minimal pairs are extremely rare; van der Kooij, 2002), so Maximal Neighborhood Density may not fully capture the phonological structure of the lexicon. For this reason, an ad- ditional neighborhood density measure (Minimal eighborhood Density, column AJ) was added that defines neighbors as signs that overlap in at least one feature of any kind with the target. The median Minimal Neighborhood Density is 780 (see Fig. 1B). Because Minimal Neighoborhood Density includes quite distant neighbors, the distribution is skewed toward more neighbors. There is also a The neighborhood density measures described above calcu- ate the number of shared sub-lexical properties irrespective of he type of property (i.e., location, movement, handshape). However, much of the linguistic work on sign languages has focused on the relationship between signs that share a partic- ular sub-lexical feature (e.g., location) and the “neighborhood density” for that sub-lexical feature (e.g., location neighbor- hood density, handshape neighborhood density; Baus, Gutiérrez-Sigut, Quer, & Carrieras, 2008; Baus, Gutiérrez & Carreiras, 2014; Corina & Emmorey, 1993; Corina & Hildebrandt, 2002; Corina & Knapp, 2006; Dye & Shih,

Related Figures (4)

Fig. 2. Frequency histograms showing the distribution of raw iconicity ratings [A] raw frequency ratings [B] of signs in ASL-LEX
Fig. 2. Frequency histograms showing the distribution of raw iconicity ratings [A] raw frequency ratings [B] of signs in ASL-LEX
Fig. 3 Frequency distribution of phonological properties in ASL-LEX. For selected fingers, m = middle finger, r= ring finger, p = pinky, i= index finge: For Sign Type, HS = handshape
Fig. 3 Frequency distribution of phonological properties in ASL-LEX. For selected fingers, m = middle finger, r= ring finger, p = pinky, i= index finge: For Sign Type, HS = handshape
Table 1 Spearman correlations among continuous lexical properties Non-parametric correlations were used because many of these properties were not normally distributed, and some of the data was ordinal *p <.05, **p <.01, ***p <.001
Table 1 Spearman correlations among continuous lexical properties Non-parametric correlations were used because many of these properties were not normally distributed, and some of the data was ordinal *p <.05, **p <.01, ***p <.001
The data are also available for browsing and searching from the ASL-LEX website, http://asl-lex.org. As depicted in Fig. 4A, signs are represented visually by nodes. Larger nodes indicate signs with higher subjective frequency. Signs are organized into parameter-based neighborhoods The entire database is available for download as a set of CSV files through the Open Science Framework (http://osf.io/ Fig. 4 Screenshots of http://asl-lex.org. The entire lexicon can be seen in [A], the lexicon filtered so that only the signs that select the index finger can b: seen in [B], and the information for the sign FURNITURE can be seen in [C] Using ASL-LEX
The data are also available for browsing and searching from the ASL-LEX website, http://asl-lex.org. As depicted in Fig. 4A, signs are represented visually by nodes. Larger nodes indicate signs with higher subjective frequency. Signs are organized into parameter-based neighborhoods The entire database is available for download as a set of CSV files through the Open Science Framework (http://osf.io/ Fig. 4 Screenshots of http://asl-lex.org. The entire lexicon can be seen in [A], the lexicon filtered so that only the signs that select the index finger can b: seen in [B], and the information for the sign FURNITURE can be seen in [C] Using ASL-LEX
Related topics:
Sign LanguagePhonologyAmerican Sign LanguageLexical FrequencyLinguistic iconicityPhonological Neighborhood Density
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