Quantitative comparison of Ugaritic and Tiberian Hebrew
7.6.3. Comparing Tiberian Hebrew and Ugaritic
The Tiberian Hebrew and Ugaritic data can be compared directly according to Association Score B for those POS sequences found in both sets of data representing the distributions in the corpus as a whole (note again the considerations in §7.5.6 in respect of the partial nature of the Ugaritic data). The sequences compared were:
• Noun + Noun
The values for Association Score B were plotted on two bar charts. Figure 7.1 gives the POS sequences with a positive association with Ugaritic univerbation, along with the Association Scores of their Tiberian Hebrew counterparts. In each case univerbation in Ugaritic is positively associated with a syntagm that has a positive association in Tiberian Hebrew either with conjunctive accentuation (Verb + Noun, Verb + Prep, Noun + Adj.), or with maqqef (Prep + Noun), or with both (Noun + Noun).
Figure 7.2 gives the POS sequences with a negative association with Ugaritic univerbation, along with the Association Scores of their Tiberian Hebrew counterparts.
Every syntagm negatively associated with univerbation in Ugaritic is also negatively associated in Tiberian Hebrew with maqqef Noun + Ptcl, with conjunctive accentuation (Ptcl + Noun) or with both maqqef and conjunctive accentuation (Noun + Verb, Noun + Prep). In three of these cases (Noun + Ptcl, Noun + Prep, Noun + Verb), the syntagm is also positively associated with disjunctive accentuation.
7.6.3.2. Verb + X syntagms
One area where Ugaritic univerbation patterns with conjunctive accentuation over against maqqef is in Verb + X (Verb + Noun, Verb + Prep) syntagms: these are positively associated with univerbation in Ugaritic, and with conjunctive accentuation in Tiberian Hebrew. By contrast, these syntagms are negatively associated with maqqef.
Table 7.15: POS sequences joined by conjunctive accent in Tiberian Hebrew (Assoc.
Score B, POS prevalence in BH corpus > 0.5%)
Figure 7.1: Syntagms positively associated with univerbation in Ugaritic, along with Tiberian Hebrew counterparts (Association Score B)
Figure 7.2: Syntagms negatively associated with univerbation in Ugaritic, along with Tiberian Hebrew counterparts (Association Score B)
It is worth recalling that a negative association does not mean that the syntagm does not occur at all in the corpus. For example, Verb + Noun sequences joined by maqqef are attested in Tiberian Hebrew despite the strongly negative correlation, e.g.:
(239) Gen 2:24
וי ֖ ִב ָא־ת ֶא שׁי ִ֔א־בָזֲעַ ֽי ⟵ (yʿzb≡ʾyšω φ) (ʾt≡ʾby=wω φ) leaves≡man obj≡father-his
‘a man shall leave his father’ (KJV)
Similarly, there are examples of Verb + Prep syntagms joined by maqqef:
(240) Job 22:24
ר ֶצ֑ ָבּ ר֥ ָפָע־לַע־תי ִשׁ ְו ⟵
(w=šyt≡ʿl≡ʿprω bṣrω φ) and=[setv≡[on≡dustpp] [goldobjp]vp]
‘Then shalt thou lay up gold as dust’ (KJV)
The negative association means that the incidence of such syntagms joined by maqqef is lower than would be expected based on the occurrence of the syntagm in the corpus as a whole.
The positive association of Verb + X syntagms with conjunctive accentuation, on the other hand, means that not only are such sequences much more frequently joined by conjunctive accents than maqqef, but that the association is greater than their frequency in the corpus as a whole would suggest.
(241) Gen 10:24
ח ַל֑ ָשׁ־ת ֶא ד֣ ַלָי ד֖ ַשׁ ְכ ַפּ ְר ַא ְו ⟵ (w=ʾrpkšdω φ) (yldω ʾt≡šlḥω φ) and=[PNsubjp] [begat obj≡PNvp]
‘And Arphachshad begat Salah’ (KJV)
• Verb + PrepP cf. (227):
(242) Gen 7:9
ַח�ֹנ־ל ֶא וּא֧ ָבּ ⟵
(bʾwω ʾl≡nḥω φ) [they_camev [to≡PNpp]vp]
‘they came to Noah’
The fact that Ugaritic patterns with Hebrew conjunctive accentuation over against maqqef in Verb + X sequences carries one of two possible implications:
• Ugaritic univerbation represents units joined at the level of the prosodic phrase;
• In the Ugaritic language, Verb + X sequences frequently formed single prosodic words, whereas this is not the case in Tiberian Hebrew.
From the present vantage point, it is hard to choose between these two possibilities.
However, in Chapter 8 evidence will be provided that implies that points in the direction of the second possibility.
7.6.4. Summary
The foregoing analysis has shown that univerbation in Ugaritic has syntagmatic affinities with both maqqef and conjunctive accentuation. This is to say that, from the perspective of Tiberian Hebrew, univerbation in Ugaritic has affinities with both prosodic wordhood and prosodic phrasehood: some syntagms that are univerbated in Ugaritic are often linked at the level of the prosodic phrase in Tiberian Hebrew, while others have a stronger affinity with prosodic word-level association. The distributional location of Ugaritic univerbation between maqqef and conjunctive accentuation in Tiberian Hebrew can alternatively be visualised using MultiDimensional scaling, to which I turn in the next section (§7.7).
7�7� Visualising morphosyntactic collocation of linking features with MDS
7.7.1. Introduction
The measures described in §7.5 can be used to visualise the morphosyntactic collocation of linking features in Tiberian Hebrew and Ugaritic. Since the relationship between the variables (in this case subcorpora, e.g. Genesis, Exodus) is calculated in terms of many dimensions (in this case, POS sequences, e.g. Noun–Noun), it is impossible to plot the exact position of each subcorpus. It is therefore necessary to reduce the number of dimensions. A helpful tool for visualising the distributions of multivariate data is MultiDimensional Scaling (MDS).5
The data processing steps are outlined at §7.7.1.1. The findings in respect of Tiberian Hebrew and Ugaritic are then presented at §7.7.3.2.
7.7.1.1. Data processing steps for obtaining MDS plots
All data was processed in Python. The data processing steps were as follows:
• Step 1: Produce a pandas DataFrame (https://pandas�pydata�org) f with:
◦ Columns labelled for POS sequences w, e.g. Noun–Noun, Verb–Noun, Noun–Verb;
5 (For an overview, see e.g. Mead (1992); see also https://en.wikipedia.org/wiki/Multidimensional_scaling, accessed 23/08/2021.
◦ Rows labelled for a subcorpora c (e.g. Bible book) and a particular linking feature j (e.g. disjunctive accent, conjunctive accent, maqqef), i.e. Genesis–Disjunctive, Genesis–Conjunctive, Genesis–Maqqef, Exodus–Disjunctive etc.
◦ Values Here two measures may be used:
► a) Proportion of Occurrences (Eq� 2) of a given POS sequence under a particular linking feature in a given subcorpus: e.g. a value of 0.7 for Noun–
Noun sequences joined by maqqef in Genesis would mean that Noun–Noun sequences represent 70% of POS sequences joined by maqqef in Genesis.
► b) Association Score (Eq� 9) of a given POS sequence (e.g. Noun–Noun) under a particular linking feature (e.g. maqqef) in a given subcorpus (e.g. Genesis):
The higher the value, the stronger the association between the POS sequence and the linking feature in the subcorpus; a value above 1 for a given POS sequence under a given linking feature in a given subcorpus means that the Proportion of Occurrences of that POS sequence under the linking feature in the subcorpus is greater than the proportion of instances of the POS sequence occurring in the population as a whole.
• Step 2: Produce a Scaled DataFrame s( f ) from the values obtained in Step 1: The values in a given row p of DataFrame f are divided by the maximum value in the row, corresponding to the highest Proportion of Occurrence/Association Score of any POS sequence under a given linking feature in a subcorpus, to obtain a value between 0 and 1. A value of 1 for a POS sequence w joined by a linking feature j in subcorpus c, wj_c means that that the sequence w has the highest Proportion of Occurrence/Association Score under linking feature j in subcorpus c.
• Step 3: Produce a distance matrix d(s(f )) giving the Euclidean distance between each subcorpus for each linking feature in terms of the scaled values produced in Step 2. Because the distances are calculated on a Scaled DataFrame, the comparison is relative to the maximum Proportion of Occurrence/Association Score of any POS sequence in a given subcorpus. Euclidean distances were calculated according to the following formula, for given rows of d(s(f)) pandq:6
( ) (
= −) (
+ −)
+⋅⋅⋅+ −( )
+⋅⋅⋅+(
−)
d p q, p q1 1 2 p q2 2 2 p qi i 2 p qn n 2 Eq. 12
• Step 4: Produce a 2D MultiDimensional Scaling (MDS) plot of the distance matrix d(s(f)). MDS plots were produced using the EcoPy package (https://ecopy.
readthedocs.io/en/latest/), and plotted with the MatPlotLib library (https://
matplotlib.org/). The method:7
6 See https://en.wikipedia.org/wiki/Euclidean_distance, accessed 23/08/2021.
7 https://ecopy.readthedocs.io/en/latest/ordination.html, accessed 23/08/2021.
Takes a square-symmetric distance matrix with no negative values as input. After finding the solution that provide the lowest stress, ecopy.MDS scales the fitted distances to have a maximum equal to the maximum observed distance. Afterwards, it uses PCA to rotate the object (site) scores so that variance is maximized along the x-axis.
The method takes a transform parameter. For the MDS given in the present study the value for this parameter was absolute. With this parameter, the method ‘Conducts absolute MDS. Distances between points in ordination space should be as close as possible to observed distances’ (https://ecopy.readthedocs.io/en/latest/ordination.
html, accessed 23/08/2021).
As already mentioned (§7.7.1), by its nature MDS involves the reduction in the number of dimensions of multivariate data. In representing a multi-dimensional reality in two dimensions there will always be a number of possibilities. How good a representation of the multi-dimensional original a given 2D representation may be expressed in terms of ‘stress’. In general, a stress value of 0.2 or above is regarded as suboptimal; if the stress figure is above this threshold, the plot should ideally be redone in higher dimensions.8
7.7.2. Overview
In this section the morphosyntactic distributions of Ugaritic graphematic univerbation and Tiberian Hebrew accentuation are compared directly using Association Score B (§7.5.7). The variable is the POS type that comes before and after the small vertical wedge, or lack of it, e.g. Noun + Noun, or Verb + Noun etc. Recall that a figure above 1 indicates that univerbation is positively associated with the combination, and a figure below 1 indicates that univerbation is negatively associated with the combination. Owing to constraints of time, the Ugaritic dataset used for this part of the investigation was restricted to the Baʿl epic (KTU 1–6).