A New Formula for Vietnamese Text Readability Assessment
Text readability has an important role in text drafting and document selecting. Researches on the readability of the text have been made long ago for English and some common languages. There are few researches in Vietnamese text readability and most of them are performed from more than two decades ago on very small corpora. In this paper, we build a new and larger corpus and use it to create a newer formula to predict the difficulty of Vietnamese text. The experimental results show that the new formula can predict the readability of Vietnamese documents with over 80% accuracy.
1 Introduction
Text readability – as defined by Edgar Dale and Jeanne Chall (Dale and Chall, 1949) – is “the sum total (including all the interactions) of all those elements within a given piece of printed material that affect the success a group of readers has with it. The success is the extent to which they understand it, read it at an optimal speed, and find it interesting.” Text readability has a huge impact on the reading and comprehending a text. Base on the readability, readers can determine whether a text is suitable for their reading ability or not. The text author(s) can also use the readability of the draft to guide readers object or have some adjustments to make it fit the toward reader. Building a model to analyze text readability has meant a lot in the scientific and practical: help scientists writing research reports more readable; support educators drafting textbooks and curricula to suit each age of students; support publishers in shaping the audience; help governments drafting legal documents to suit the majority of citi-
zens; or to assist manufacturers in preparing user guide for their products. . . In addition, text readability can effectively support in choosing appropriate curriculums when teaching language for foreigners.
Researches on text readability have begun since the early of the 20th century, most of them are for English and some common languages. Most famous studies in text readability are creating linear functions to assess and grade documents like Dale-Chall formula (Dale and Chall, 1949), Flesch Reading Ease formula (Flesch, 1949), FleschKincaid formula (Kincaid et al., 1975), Gunning Fog formula (Robert, 1952), SMOG formula (Laughlin, 1969). . . In Vietnamese, there are only two studies on text readability of Liem Thanh Nguyen and Alan B. Henkin in 1982 and 1985. Both these two researches focus on examining relations between statistical characteristics at words level and at sentences level and text readability. However, these two works only evaluate on small corpora of 20 documents (Nguyen and Henkin, 1982) and 54 documents (Nguyen and Henkin, 1985). Since these studies, there is almost no other publication on Vietnamese text readability.
In this paper, we mainly focus on creating a new formula for Vietnamese text readability assessment base on a self-built corpus with a large number of documents. Following this, the remaining of this paper is presented as below: we first present some famous formulas in English text readability as related works in section 2; we then present our works on building a corpus and creating formulas to assess Vietnamese text readability along with some experiments and results in section 3; finally, section 4 presents our discussions and conclusions.
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2 Related works
Studies on text readability have begun since the early of the 20th century. Up to now, there are thousands of works in this field. In this section, we will describe some famous formulas for English text readability assessment and two formulas for Vietnamese. First is the new Dale-Chall formula (Chall and Dale, 1995). This is a very famous readability formula that provides the comprehension difficulty score of an English document. This uses a list of 3000 words that students in fourth-grade could understand. Words that not in this list are considered as difficult words. The score is calculated as Equation 1:
RS = 0.1579 ∗ (PDW ) + 0.0496 ∗ASL (1)
where RS is the Reading Grade of a reader who can comprehend the text at 3rd grade or below, the higher RS, the more difficult the text; PDW is Percentage of Difficult Words; and ASL is Average Sentence Length in words. The list of 3.000 words that student in fourth-grade could understand was replaced by the list of 3.000 frequent Vietnamese words to experiment in Vietnamese corpus. The second formula is Flesh Reading Ease (Flesch, 1949). This is an easy formula for measuring English text readability and is used by many US government agencies like US Department of Defense. . . It is also integrated into the Microsoft Word - the most popular word processor - from version 2007 to help users checking and controlling the readability of the document. The formula is defined as Equation 2:
RE = 206.835−(1.015 ∗ASL)−(84.6 ∗ASW ) (2) whereRE is the Readability Ease of the text;ASL is Average Sentence Length in words; and ASW is the average number of syllables per word. Like the name of the formula, the higherRE, the easier the document. Next is the Flesch-Kincaid grade level readability formula (Kincaid et al., 1975) for English. This formula is best suited for education and is also integrated into the Microsoft Word. It is defined as Equation 3:
FKRA = (0.39xASL)+(11.8 ∗ASW )−15.59 (3)
where FKRA is the Flesch-Kincaid Reading Age, indicates the grade-school level that student can read and comprehend; ASL is Average Sentence Length in words; andASW is the average number of syllables per word. Because this formula determines the grade-school level of the text so the higher FKRA, the more difficult the text. Continue is the Gunning Fog index formula (Robert, 1952). This formula is developed by Robert Gunning – an American textbook publisher. It is defined as Equation 4:
Grade level = 0.4 (ASL+ PHW ) (4)
where ASL is Average Sentence Length in words; and PHW is Percentage of Difficult Words - the percentage of words which have three or more syllables that are not proper nouns, combinations of easy words or hyphenated words, or two-syllables verbs made into three with -es and -ed endings. Similar to the Flesch-Kincaid grade level, the higher Gunning Fog index, the more difficult the text. Next is the SMOG formula (Laughlin, 1969). This formula estimates the years of education a person needs to understand a specific text and is widely used in checking health messages. The higher SMOG value, the more difficult the text. The formula is:
SMOG grade = 3 + √ Polysyllable Count (5)
In Vietnamese, as mentioned in Section 1, there are only two researches on text readability of Liem Thanh Nguyen and Alan B. Henkin in 1982 and 1985 (Nguyen and Henkin, 1982, 1985). The first formula is defined as Equation 6
RL = 2WL+ 0.2SL− 6 (6)
where RL is Readability Level of the text; WL is Average Word Length in characters; and SL is Average Sentence Length in words. The second formula was revised from the first one with the additional role of the ratio of difficult words in the document:
RL = 0.27WD + 0.13SL+ 1.74 (7)
where RL is Readability Level of the text, the higher RL value, the more difficult the text; WD is Word Difficulty; and SL is Average Sentence Length in words.
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However, these two works only evaluate on small corpora of 20 documents (Nguyen and Henkin, 1982) and 54 documents (Nguyen and Henkin, 1985). Since these studies, there is almost no other publication on Vietnamese text readability. Furthermore, as proposed by Professor Lucius Adelno Sherman, languages are still changing over time (Sherman, 1893), so researches on readability will still continue.
3 Method
3.1 Building corpus
As mentioned in sections 1 and 2, there are only two publications on Vietnamese text readability and both of them use only small corpora with 20 and 54 documents (Nguyen and Henkin, 1982, 1985). So in our research, we built another corpus with a larger amount of document for examining. 1,000 documents were collected into 3 categories of difficulty from various sources with the following criteria:
1. Easy documents: including documents written for children or by children or just need people who are studying at primary schools or having maximum primary education to read and understand. These documents were mainly collected from primary school textbooks, primary sample essays, fairy-tales, stories for babies. . .
2. Normal documents: they are documents written for middle and high school students, or documents which only need people with high school education to be readable and understandable. Most documents in this category were collected from textbooks and general newspapers.
3. Difficult documents: including documents written for college students, specialized documents, scientific paper. . . which need high or specialized education to be readable and understandable. These documents were collected from university textbooks, specialized documents, political theory articles, language and literary articles, law and legal documents. . .
Ten experts were asked to evaluate collected documents. They are Vietnamese language specialists, current or former Vietnamese literature teacher - who has much knowledge and experiment in using and teaching Vietnamese. Each document was ensured to be evaluated by 3 experts with the following instruction: each person carefully reads each given document and gives a score for that document: a) If that document is easy enough for children and people with just primary school education to read and understand, give the Score = 1; b) Score = 2 if almost all adults and people with average education can read and understand; c) If documents need people with high or specialized education to be readable and understandable, give the Score = 3. The overall scores of each document are used to re-category that document:
1. If the document got 3 equal scores: that document will be categorized according to the category of the score.
2. If the document got 2 equal scores and one different: that document will be put to the category of the 2 equal scores.
3. If the document got 3 different scores: that document will be filtered out of the corpus.
Easy Normal Difficult Overall No. documents 235 413 348 996 No. sentences 4,006 13,772 24,646 42,424 Average sentence length in word 13.59 19.24 22.71 19.12 Average sentence length in syllable 16.48 25.04 33.66 26.03 Average sentence length in character 79.50 124.39 173.54 130.97 Average word length in syllable 1.21 1.30 1.48 1.34 Average word length in character 5.82 6.44 7.62 6.71
Table 1: Statistics numbers of built corpus.
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Finally, for some statistic later, we assign the readability value of 1 for all documents in the Easy category, 2 for the Normal and 3 for the Difficult category. Table 1 presents some statistic number of the final corpus. To ensure that our corpus is reliable, we calculated the Fleiss’ kappa score (Fleiss, 1971). This is a statistical measure for assessing the agreement between a fixed number of raters when classifying items. The Fleiss’ kappa measure is usually used in natural language processing to assess the reliability of agreement between references in the corpus or between manual annotation by raters/experts. The K value is 0.422, which demonstrates that there is a moderate agreement between annotators so the corpus is reliable.
3.2 Features
In this part, we will describe some features that are commonly used in text readability assessment. Average sentence length: the average sentence length of a text is one of the simplest and common characteristic when measuring text readability. In this paper, we examined three types of average sentence length as Equation 8, 9 and 10: Average Sentence Length in Words (ASLW):
ASLW = word count
sentence count (8)
Average Sentence Length in Syllables (ASLS):
ASLS = syllable count sentence count
(9)
Average Sentence Length in Characters (ASLC):
ASLC = character count sentence count
(10)
Average word length: two types of average word length were examined in this study as Equation 11 and 12: Average Word Length in Syllables (AWLS):
AWLS = syllable count word count
(11)
Average Word Length in Characters (AWLC):
AWLC = character count word count
(12)
In Vietnamese, word maybe monosyllabic or polysyllabic (compound word) with a whitespace
between each syllable; each syllable is a combination of letters with or without tonal and word marks. For example: the word “nghe” (listen) is a monosyllable with four letters (n, g, h, and e); the word “chạy” (run) is a monosyllable with four letters (c, h, a, y) and a tonal mark (.); the word “thời gian” (time) is a polysyllable (2 syllables “thời” and “gian”) with eight letters (t, h, o, i, g, i, a, n), a word mark ( ’ ), a tonal mark ( ` ) and a whitespace.
Percentage of difficult words: in many studies, the percentage of difficult words is an important feature when evaluating text readability. However, create the easy or difficult word list needs a lot of effort, so most researches used frequent word list as a replacement: if a word does not appear in the frequent list, it will be considered as a difficult word. In this study, we used two frequent lists: the first is top 1,000 frequent words extracted from the frequent word list of Dien and Hao (Dinh and Hao, 2015); and the second is 1,000 frequent words extracted from all easy documents of our built corpus. The percentage of difficult words is calculated as Equation 13:
PDWi = difficult word count
word count (13)
In this paper, PDW1 stands for a percentage of difficult words calculated using Dien and Hao’s list and PDW2 stands for a percentage of difficult words calculated using our easy list. Not only the percentage of difficult words, but also the percentage of difficult syllables was examined in our study. We also used two frequent lists: the top 1,000 frequent syllables extracted from the list of Dien and Hao (Dinh and Hao, 2015); and the 1,000 frequent syllables extracted from all easy documents of our corpus. The percentage of difficult syllables is calculated as Equation 14:
PDWi = difficult syllable count
syllable count (14)
Similar to word, in this paper, PDS1 and PDS2 stand for a percentage of difficult words calculated using Dien and Hao’s list and our easy list respectively.
3.3 Create formula
The first thing is finding which features are suitable for predicting text readability through corre-
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TR ASLW ASLS ASLC AWLS AWLC PDS1 PDW1 PDS2 PDW2 TR 1 ASLW 0.567 1 ASLS 0.675 0.970 1 ASLC 0.695 0.955 0.997 1 AWLS 0.770 0.483 0.669 0.699 1 AWLC 0.774 0.497 0.674 0.716 0.979 1 PDS1 0.051 -0.116 -0.140 -0.132 -0.107 -0.073 1 PDW1 0.103 -0.069 -0.029 -0.018 0.156 0.156 0.680 1 PDS2 0.466 0.201 0.237 0.251 0.328 0.355 0.666 0.602 1 PDW2 0.786 0.438 0.562 0.589 0.785 0.793 0.340 0.467 0.782 1
Table 2: Correlation between each feature with the text readability and between features together (TR is the Text Readability).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Average Coefficient values
Const -0.8141 -0.6767 -0.6372 -0.7777 -0.7419 -0.7295 ASLC 0.0037 0.0040 0.0043 0.0040 0.0040 0.0040 AWLC 0.2105 0.1779 0.1699 0.2032 0.1911 0.1905 PDW2 2.6960 2.7974 2.7224 2.6370 2.7208 2.7147
Accuracy Easy 0.7234 0.7021 0.8511 0.7234 0.8085 0.7660 Normal 0.8313 0.9036 0.8537 0.8537 0.8313 0.8571 Difficult 0.7681 0.7857 0.8000 0.8000 0.7826 0.7845 Overall 0.7839 0.8150 0.8342 0.8040 0.8090 0.8102
Table 3: Coefficient values and accuracy of all formulas.
lation analysis. Table 2 shows the correlation coefficients between each feature with the text readability and between features together performed on our corpus.
We can see that the ASLS, ASLC, AWLS, AWLC and PDW2 are high correlated with the text readability. However, they are also high correlated with some others. To choose which features to put in the formula, we select sequentially features from the highest correlation with text readability to the lowest and remove features have high correlation coefficient with selected features. Through some experiments, we decided to use the threshold 0.9 for removing features: if a feature has the correlation coefficient with selected features greater than or equal to 0.9, that feature will not be cho-
sen. Following this, three features were selected: ASLC, AWLC, and PDW2. The features AWLS and ASLS are high correlated with AWLC and ASLC so they were not chosen. The selected features were used as predictors to perform multiple regression analysis with Text Readability as the criterion. The purpose is to find coefficient values of these features to form a formula for predicting text readability. The general formula is:
Readability = A1ASLC +A2AWLC
+A3PDW2 +A4 (15)
WithA1 toA4 are coefficient values. In this study, we divided our corpus into five equal parts for cross-fold analyzing. In each fold, four part were
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selected to perform regression analysis and the coefficient values of each fold will be used to predict the readability of the remaining part using Formula 15. The predicted values were rounded to the nearest unit and compared to the expert evaluated readability for accuracy assessment. Finally, the average coefficient value of each predictor was used to form the final text readability formula. The final formula is:
Readability = 0.004ASLC + 0.1905AWLC
+2.7147PDW2− 0.7295 (16)
Table 3 presents coefficient values and accuracy of all formulas.
4 Discussion and conclusion
From the Table 2, we can see that word length, sentence length and the percentage of difficult words still play an important role in measuring the text readability. The Percentage of difficult syllables (PDS1) and words (PDW1) calculated on the lists extracted from Dien and Hao’s lists (Dinh and Hao, 2015) are low correlated with the Text readability of the built corpus. The main reason is the lists of Dien and Hao were statistically analyzed from the corpus mainly collected from newspapers, which are mostly texts with normal readability. So the Dien and Hao’s lists and other frequent lists which were collected from normal texts may not be good replacements for the easy word and syllable list. In the Table 3, we can see that the final text readability formula can accurately predict more than 76% of the easy texts, more than 85% of the normal documents and more than 78% of the difficult texts. Overall, the formula can predict the Vietnamese text readability with the accuracy up to 81%. This is a good result and can be applied in practice. In this paper, we have presented our work on creating a new large corpus for Vietnamese text readability assessing. We also used the corpus to create a new formula for predicting Vietnamese text readability. Experiments performed on the corpus using created formula shows that the formula can predict the readability of Vietnamese text with high accuracy. For the future works, other corpora will be built with more detailed levels of difficulty and
for more specific domains. Other deeper features like part-of-speech, sentence structure, discourse. . . will be examined to create more precise formula(s). Some machine learning methods will be examined to create some classifier for automatically Vietnamese text readability assessment.
The authors present a new formula for assessing readability of Vietnamese texts. The formula is developed based on a multiple regression analysis with three features. Furthermore, the authors have developed and annotated a new text corpus with three readability classes (easy, middle, hard).  Research on languages other than English is interesting and important, especially when it comes to low-resource languages. Therefore, the corpus might be a nice additional resource for research (but it seems that the authors will not publish it - is that right?). However, I don't think the paper is convincing in its current shape or will influence future research. Here are my reasons:  - The authors provide no reasons why there is a need for delevoping a new formula for readability assessments, given that there already exist two formulas for Vietnamese with almost the same features. What are the disadvantages of those formulas and why is the new formula presented in this paper better?  - In general, the experimental section lacks comparisons with previous work and analysis of results. The authors claim that the accuracy of their formula (81% on their corpus) is  good and can be applied in practice . What would be the accuracy of other formulas that already exist and what are the pros and cons of those existing formulas compared to the new one?  - As mentioned before, an analysis of results is missing, e.g. which word   sentence lengths   number of difficult words are considered as easy middle hard by their model?  - A few examples how their formula could be applied in a practical application would be nice as well.  - The related work section is rather a  background  section since it only presents previously published formulas. What I'm missing is a more general discussion of related work. There are some papers that might be interesting for that, e.g., DuBay 2004:  The principles of readability , or Rabin 1988:  Determining difficulty levels of text written in languages other than English   - Since Vietnamese is syllable-based and not word-based, I'm wondering how the authors get  words  in their study. Do they use a particular approach for merging syllables? And if yes, which approach do they use and what's the accuracy of the approach?  - All in all, the content of the paper (experiments, comparisons, analysis, discussion, related work) is not enough for a long paper.  Additional remarks:  - The language needs improvements  - Equations: The usage of parentheses and multiplying operators is inconsistent  - Related works section: The usage of capitalized first letters is inconsistent