Application of Lexical Features Towards Improvement of Filipino Readability Identification of Children's Literature

Proper identification of grade levels of children's reading materials is an important step towards effective learning. Recent studies in readability assessment for the English domain applied modern approaches in natural language processing (NLP) such as machine learning (ML) techniques to automate the process. There is also a need to extract the correct linguistic features when modeling readability formulas. In the context of the Filipino language, limited work has been done [1, 2], especially in considering the language's lexical complexity as main features. In this paper, we explore the use of lexical features towards improving the development of readability identification of children's books written in Filipino. Results show that combining lexical features (LEX) consisting of type-token ratio, lexical density, lexical variation, foreign word count with traditional features (TRAD) used by previous works such as sentence length, average syllable length, polysyllabic words, word, sentence, and phrase counts increased the performance of readability models by almost a 5% margin (from 42% to 47.2%). Further analysis and ranking of the most important features were shown to identify which features contribute the most in terms of reading complexity.Comment: 8 tables, 1 figure. Presented at the Philippine Computing Science Congress 202

Automated Readability Assessment for Spanish e-Government Information

This paper automatically evaluates the readability of Spanish e-government websites. Specifically, the websites collected explain e-government administrative procedures. The evaluation is carried out through the analysis of different linguistic characteristics that are presumably associated with a better understanding of these resources. To this end, texts from websites outside the government websites have been collected. These texts clarify the procedures published on the Spanish Government"s websites. These websites constitute the part of the corpus considered as the set of easy documents. The rest of the corpus has been completed with counterpart documents from government websites. The text of the documents has been processed, and the difficulty is evaluated through different classic readability metrics. At a later stage, automatic learning methods are used to apply algorithms to predict the difficulty of the text. The results of the study show that government web pages show high values for comprehension difficulty. This work proposes a new Spanish-language corpus of official e-government websites. In addition, a large number of combined linguistic attributes are applied, which improve the identification of the level of comprehensibility of a text with respect to classic metrics.Work supported by the Spanish Ministry of Economy, Industry and Competitiveness, (CSO2017-86747-R)

Measuring text readability with machine comprehension: a pilot study

International audienceThis article studies the relationship between text readability indice and automatic machine understanding systems. Our hypothesis is that the simpler a text is, the better it should be understood by a machine. We thus expect to a strong correlation between readability levels on the one hand, and performance of automatic reading systems on the other hand. We test this hypothesis with several understanding systems based on language models of varying strengths, measuring this correlation on two corpora of journalistic texts. Our results suggest that this correlation is rather small that existing comprehension systems are far to reproduce the gradual improvement of their performance on texts of decreasing complexity

Language learning and teaching with Swedish FrameNet++ : Two examples

Peer reviewe

Examining the Part-of-speech Features in Assessing the Readability of Vietnamese Texts

The readability of the text plays a very important role in selecting appropriate materials for the level of the reader. Text readability in Vietnamese language has received a lot of attention in recent years, however, studies have mainly been limited to simple statistics at the level of a sentence length, word length, etc. In this article, we investigate the role of word-level grammatical characteristics in assessing the difficulty of texts in Vietnamese textbooks. We have used machine learning models (for instance, Decision Tree, K-nearest neighbor, Support Vector Machines, etc.) to evaluate the accuracy of classifying texts according to readability, using grammatical features in word level along with other statistical characteristics. Empirical results show that the presence of POS-level characteristics increases the accuracy of the classification by 2-4%

Approches à base de fréquences pour la simplification lexicale

National audienceLa simplification lexicale consiste à remplacer des mots ou des phrases par leur équivalent plus simple. Dans cet article, nous présentons trois modèles de simplification lexicale, fondés sur différents critères qui font qu'un mot est plus simple à lire et à comprendre qu'un autre. Nous avons testé différentes tailles de contextes autour du mot étudié : absence de contexte avec un modèle fondé sur des fréquences de termes dans un corpus d'anglais simplifié ; quelques mots de contexte au moyen de probabilités à base de n-grammes issus de données du web ; et le contexte étendu avec un modèle fondé sur les fréquences de cooccurrences. ABSTRACT Studying frequency-based approaches to process lexical simplification Lexical simplification aims at replacing words or phrases by simpler equivalents. In this paper, we present three models for lexical simplification, focusing on the criteria that make one word simpler to read and understand than another. We tested different contexts of the considered word : no context, with a model based on word frequencies in a simplified English corpus ; a few words context, with n-grams probabilites on Web data, and an extended context, with a model based on co-occurrence frequencies. MOTS-CLÉS : simplification lexicale, fréquence lexicale, modèle de langue

Neural versus Phrase-Based Machine Translation Quality: a Case Study

Within the field of Statistical Machine Translation (SMT), the neural approach (NMT) has recently emerged as the first technology able to challenge the long-standing dominance of phrase-based approaches (PBMT). In particular, at the IWSLT 2015 evaluation campaign, NMT outperformed well established state-of-the-art PBMT systems on English-German, a language pair known to be particularly hard because of morphology and syntactic differences. To understand in what respects NMT provides better translation quality than PBMT, we perform a detailed analysis of neural versus phrase-based SMT outputs, leveraging high quality post-edits performed by professional translators on the IWSLT data. For the first time, our analysis provides useful insights on what linguistic phenomena are best modeled by neural models -- such as the reordering of verbs -- while pointing out other aspects that remain to be improved