Combining a statistical language model with logistic regression to predict the lexical and syntactic difficulty of texts for FFL

Thomas, François

doi:10.3115/1609179.1609182

Cited by 17 publications

(13 citation statements)

References 23 publications

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“…Readability research is no exception to this rule. Since readability prediction was initially designed to identify reading material suited to the reading competence of a given individual, most of the existing data sets are drawn from textbooks and other sources intended for di↵erent competence levels (François, 2009;Heilman et al, 2008;Feng et al, 2010). Although recent annotation e↵orts have also tackled other text types (e.g.…”

Section: Etcetera)mentioning

confidence: 99%

“…Nevertheless, we can deduct from previous research that features which are lexical in nature, such as language modeling features, have a strong predictive power. Besides various features, more intricate prediction methods such as Naive Bayes classifiers (Collins- Thompson & Callan, 2004), logistic regression (François, 2009) and support vector machines (Schwarm & Ostendorf, 2005;Feng et al, 2010;Tanaka-Ishii et al, 2010) have come into use.…”

Section: Related Workmentioning

confidence: 99%

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Using the crowd for readability prediction

Clercq¹,

Hoste

Desmet³

et al. 2012

Nat. Lang. Eng.

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While human annotation is crucial for many natural language processing tasks, it is often very expensive and time-consuming. Inspired by previous work on crowdsourcing we investigate the viability of using non-expert labels instead of gold standard annotations from experts for a machine learning approach to automatic readability prediction. In order to do so, we evaluate two di↵erent methodologies to assess the readability of a wide variety of text material: a more traditional set-up in which expert readers make readability judgments and a crowdsourcing set-up for users who are not necessarily experts. To this purpose two assessment tools were implemented: a tool where expert readers can rank a batch of texts based on readability and a lightweight crowdsourcing tool which invites users to provide pairwise comparisons.To validate this approach, readability assessments for a corpus of written Dutch generic texts were gathered. By collecting multiple assessments per text, we explicitly wanted to level out a readers background knowledge and attitude. Our findings show that the assessments collected through both methodologies are highly consistent and that crowdsourcing is a viable alternative to expert labeling. This is good news as crowdsourcing is more lightweight to use and can have access to a much wider audience of potential annotators.By performing a set of basic machine learning experiments using a feature set which mainly encodes basic lexical and morphosyntactic information, we further illustrate how the collected data can be used to perform text comparisons or to assign an absolute readability score to an individual text. We do not focus on optimizing the algorithms to achieve the best possible results for the learning tasks, but carry them out to illustrate the various possibilities of our data sets. The results on the di↵erent data sets, however, show that our system outperforms the readability formulas and a baseline language modeling approach. Using the Crowd for Readability Prediction 3We conclude that readability assessment by comparing texts is a polyvalent methodology, which can be adapted to specific domains and target audiences if required.

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Section: Etcetera)mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Using the crowd for readability prediction

Clercq¹,

Hoste

Desmet³

et al. 2012

Nat. Lang. Eng.

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“…Another possibility is to use extensive language-specific preprocessing or word lists (Feng, Elhadad, & Huenerfauth, 2009;François, 2009;Heilman, Collins-Thompson, & Eskenazi, 2008). A different strategy is to use machine learning methods, such as classification (e.g., Si & Callan, 2001;Collins-Thompson & Callan, 2005;Heilman, Collins-Thompson, Callan, & Eskenazi, 2007;Suominen et al, 2008;Petersen & Ostendorf, 2009).…”

Section: Automatic Approaches For Text Difficulty Assessmentmentioning

confidence: 98%

Assessing user-specific difficulty of documents

Paukkeri

Ollikainen

Honkela

2013

Information Processing & Management

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“…Advancements in these elds have introduced more intricate prediction methods such as Naïve Bayes classi ers (Collins-Thompson and Callan 2004), logistic regression (François 2009) and support vector machines (Schwarm and Ostendorf 2005;Feng et al 2010;Tanaka-Ishii et al 2010) -and especially more complex features. Rather than a sole reliance on super cial text characteristics, the added value of features measuring lexical complexity based on n-gram modelling (Schwarm and Ostendorf 2005;Pitler and Nenkova, 2008;Kate et al 2010) or those relying on deep syntactic parsing (Schwarm and Ostendorf, 2005) have been corroborated repeatedly in the computational approaches to readability prediction that have surfaced in the last decade (Heilman et al 2007;Petersen and Ostendorf, 2009;Nenkova et al 2010).…”

Section: The Readability Interfacementioning

confidence: 99%

Stylene: an Environment for Stylometry and Readability Research for Dutch

Daelemans

Clercq

Hoste

2017

CLARIN in the Low Countries

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We describe an educational demonstration interface and tools for stylometry (authorship attribution and pro ling) and readability research for Dutch. The Stylene system consists of a popularisation interface for learning about stylometric analysis, and of web-based interfaces to so ware for readability and stylometry research aimed at researchers from the humanities and social sciences who do not want to develop or install such so ware themselves. IntroductionThe last decade has seen a marked increase in research on computational stylometry, the subarea of natural language processing that concerns itself with the categorisation of texts according to the psychological and sociological properties of their authors. Also called text pro ling, this research tries to develop systems, mostly based on text analytics techniques, that combine natural language processing and machine learning methods. These systems are trained to determine whether the author of a text is male or female, their education level, region of origin, personality, and even mental health, whether they are a native speaker or not, and many other potentially useful attributes. Of course, authorship attribution research has existed for a long time, and is in a sense the limit case of computational stylometry: supposing that everyone has a unique combination of demographic, psychological and idiosyncratic style properties, this would be their idiolect or 'stylome' (Van Halteren et al, 2005;Coulthard, 2004), and it should be possible to assign texts of unknown authorship to speci c authors provided that models of their stylome exist.

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Combining a statistical language model with logistic regression to predict the lexical and syntactic difficulty of texts for FFL

Cited by 17 publications

References 23 publications

Using the crowd for readability prediction

Using the crowd for readability prediction

Assessing user-specific difficulty of documents

Stylene: an Environment for Stylometry and Readability Research for Dutch

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