The Diachronic analysis of regularization

Verbal changes are diachronically attested to be unidirectional, towards regularization only (Fries 1940; Pinker 1999; Lieberman et al. 2007; Michel et al. 2011 among other). The more infrequent the irregulars are, the more they are regularized. According to the dual mechanism approach, this is due to weak representations of these verbs in the associative memory that make them harder to be accessed and thus easier to be regularized. Lieberman et al. (2007) insist that frequency plays a clear role in regularization processes. They demonstrate that the number of IVs has gradually declined over the past centuries. In their study, they find that of the 177 IVs that existed in Old English only 98 are still irregular today. They conclude that IVs with high frequency are more prone to remain irregular over time while less frequently used ones are more prone to become regular. Also, they conclude that when frequency is accounted for, regularization rates are increasing and thus not constant over time (for more details see chapter 2).

The results of the analyses in sections 4.3 and 4.4 revealed that current verbal changes are synchronically unidirectional, towards regularization only, favouring the dual mechanism

approach. The aim of this section is to investigate verb regularization in Contemporary English from the diachronic perspective. I explore whether or not recent regularization processes are constant over time in the selected sample mentioned below. So, the following question is addressed:

 In Contemporary English, are verbal changes towards regularization taking place constantly over time?

To investigate whether or not IVs are regularized regularized constantly over time, I choose the sample of this diachronic analysis from the WebCorp corpus that covers the period January 1995-December 2010. This period (16 years) is divided into two-time spans: the old span (1995-2002) and the new one (2003-2010). I select the same IVs of the sample used in the question 1 (see appendix 2). Then, in the old and new spans, word frequencies of IVs split by form and frequency are collected from the selected sample. Similarly, word frequencies of RFs split by form and frequency are collected in the old and new spans from the selected sample to draw a comparison between the two spans (see appendices 8 and 9 and for more details see chapter 3). By doing so, it can be investigated whether or not verbal changes have a constant tendency towards regularization over time. I will explore whether in the new span the regularization rate of IVs with low frequency is higher than the one in the old span. If that will be the case, there will be evidence supporting the claim of the dual mechanism approach stating that IVs with low frequency are regularized more often than IVs with high frequency as a result of retrieval failures from the associative memory.

By looking at table 25 and table 26 respectively, a general idea about frequency distributions of the verbs split by type, form and frequency in the old and new spans is obtained. The two tables demonstrate word frequencies of IVs and RFs in the two spans from the selected sample.

In addition, I have calculated relative frequency of RFs, as word frequencies of RFs depend on the size of the selected sample.

Table 25: Frequency distributions of IVs and RFs in the old span from the selected sample Type / Form High frequency Verbs Low frequency Verbs Total

Word

IVs / past 49,951 101 50,052

RFs/ past 130 0.26% 76 43% 206 0.41%

IVs / perfect 19,671 242 19,913

RFs/ perfect 85 0.43% 187 44% 272 1.35%

Table 26: Frequency distributions of IVs and RFs in the new span from the selected sample Type / Form High frequency Verbs Low frequency Verbs Total

Word

IVs 13,133,375 56,844 13,190,219

RFs 64,324 0.49% 49,019 46% 113,343 0.85%

IVs / past 9,310,000 15,018 9,325,018

RFs/ past 36,914 0.39% 15,138 50% 52,052 0.56%

IVs / perfect 3,823,375 41,826 3,865,201

RFs/ perfect 27,410 0.71% 33,881 45% 61,291 1.56%

Table 25 and table 26 display that the difference between total word frequencies of IVs in the old and new data is large. Word frequency of IVs in the old span is 69,965 that is lower than the one in the new span (13,190,219). But, this difference will not prevent us to do the statistical analysis for testing significance of the difference. A linear mixed model that will be conducted later is good to handle two different samples with two different proportions.

Let’s compare regularization processes in the old span. Table 25 shows that word frequency of RFs with low frequency (263) is higher than the one with high frequency (215). Similarly, the regularization rate in low frequency group (43%) is high compared to that one in the high group (only 0.31%.). Focusing on form, the regularization rates of both forms in low frequency group are higher than the ones in high frequency group (low: 43% for the past form and 44%

for the perfect form versus high: 0.26% for the past form and 0.43% for the perfect form).

Considering regularization processes in the new span, however, table 26 display that word frequency of RFs with low frequency (49,019) is lower than the one with high frequency (64,324). Yet, the regularization rate in low frequency group (46%) is higher than the one in high frequency group (only 0.49%.). Likewise, in the past and perfect forms, the regularization rates in low frequency group (50% and 45% respectively) are higher than the ones in high frequency group (0.39% and 0.71% respectively).

If we look at regularization processes in the old and new spans, there is an indication in data that there are slight increases in word frequencies of RFs with low frequency (old: 43% versus new: 46%) and with high frequency (old: 0.31% versus new: 0.49%). Thus, the regularization rate of IVs with low frequency in the new span is somewhat higher than the one in the old span.

The differences in frequency distributions of RFs in both spans of our sample suggest a relationship between regularization and word frequency over time, as predicted Lieberman et al. (2007) and supporters of the dual mechanism approach: IVs with low frequency are regularized more often than IVs with high frequency. Thus, these verbal changes in the direction of regularization may be not taking place constantly over time.

A statistical model is conducted to explore the effects of time, frequency and form on relative frequencies of the verbs in the selected sample. A linear mixed model was adopted, where relative frequency was considered as a dependent variable and I included the variables:

time (with two levels: old and new), frequency (with two levels: high and low) and form (with two levels: past and perfect) as fixed factors. The results of the model disclose that the main effects for time (β = -0.01, t = -0.54, p = 0.59) and form (β =0.03, t = 1.20, p = 0.23), in addition to the effects for the interaction between them (β = -0.01, t = -0.49, p = 0.62) are not significant.

Only the main effect for frequency (β = 0.14, t = 4.28, p = 2.47e-05) is significant. Nevertheless, the effects of its interaction with time and form (β = 0.02, t = 0.47, p = 0.63) are not significant.

The lack of the effect of time in this model indicates that there is no impact of time on regularization processes. Hence, I conclude that the rates of verbal changes are constant between the old and new spans in the selected sample of this study. Since the interaction with time and frequency is not significant, I may also conclude that what really matters in regularization is frequency. This means that not only does the time has no significant effect on regularization itself, but also it does not affect the larger and significant effect of frequency on regularization. Thus, verbal changes towards regularization are constant over time in the sample of this study. These results are incompatible with results of the study of Lieberman et al. and the dual mechanism view stating that verbal changes towards regularization are not constant over time. From and the dual mechanism perspective, IVs with low frequency are increasingly regularized and moving to be more general. Nevertheless, a small difference between old span and new spans of the selected sample is reported, which goes in the direction predicted by the dual mechanism approach. This suggests that even if we take a larger sample and we find a significant effect of time on regularization, this effect will always be much smaller in magnitude than the one of frequency on the rate of regularization.

To conclude, we do not find statistical evidence that the rate of regularization in both low and high frequency verbs is not constant over time. Next, we will investigate the relationship between word frequency and irregularization over time to provide further evidence either with or against single and dual mechanism approaches.