1. Automatic Readability Evaluation Using a Neural Network Vivaek Shivakumar October 29, 2009

2. Background and Purpose Readability – how difficult it is to read and comprehend a text – used in educational settings, grade-level reading evaluation Traditional readability formulas – Invented in 20 th Century before the computer age – Use primitive surface linguistic features – Still widely used, even in computer applications – e.g., Flesch-Kincaid Grade Lv.= – Dale-Chall Raw Score = 0.1579 %DW + 0.0496 AvSL + 3.6365

3. Background and Purpose Real measure of readability factors in – Surface features (e.g., syllables per word, average sentence length) – Syntactic features (sentence structure, e.g., number of subordinate clauses) Parse tree size (e.g., Feng, 2009) – Semantic features (meanings, e.g., lexical density) – *Pragmatics (context) (out of project scope)

4. Background and Purpose Goal: create a model to give a more accurate score of readability of text using sophisticated techniques – Machine learning, e.g., neural networks: can be used to create a model using textual features as inputs Supervised – using state grade level standards assessment tests for training set

5. Development Neural Network – (still to be implemented) – Will be supervised – training set: reading passages from state and national grade level assessments – Grade levels “teach” the model to get more accurate – The neural network readability model should reflect the relationships between the different inputs that will be used

6. Development Criteria/Features to be used as inputs (possible) – Average word length in syllables – Average sentence length in words – Average sizes of sentence's parse/dependency trees – Lexical density (index based on frequency of words in text compared to in English in gen.) – common/uncommon words – Other syntactic features such as the presence of certain dependency types, etc.

7. Development Surface feature statistics (e.g. word/sentence lengths) and percentage of uncommon words* – Trivial to implement *not finished Parse/Dependency trees – Using Stanford Parser (or another if faster) – Output is analyzed from easy-to-read format Neural network – Not trivial to implement – bulk of development

8. Development Example of problem of working with natural language: syllable demarcation irregularities Implementation used to count syllables: – Each group of consecutive vowels (a,e,i,o,u) counts towards a syllable, with the following exceptions: Final -ES, -ED, and -E are not counted as syllables (besides -LE, which is). The letter “y” is a vowel unless it starts a word or follows another vowel. Any word of three letters or less counts as one syllable.

9. Preliminary Testing Evaluating three readability formulas vs. “actual” grade levels – same with dependency/parse tree sizes – Investigate whether there is a relationship, and if so how strong Texts used: same as for neural network training set – 92 texts at various grade levels

10. Results of Prelim. Testing

12. Analysis of Prelim. Results Dependency and Parse tree sizes are very closely linearly associated – Makes sense to only use one or the other in neural network – All of the three readability formulas show some association with grade level – surface features are useful but not alone – None are consistent – high deviation – all are unreliable

13. Expected Results Ideally, neural network learns to evaluate U.S. Grade level of a given text with a significantly greater accuracy and precision than the existing formulas do