[PDF] Top 20 Learning to Predict Readability using Diverse Linguistic Features
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Learning to Predict Readability using Diverse Linguistic Features
... MRP readability corpus, their utility is somewhat tailored to this specific ...lexical features described in subsection ...genre-based features. Out of the language model features described in ... See full document
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Using Linguistic Features to Predict Readability of Short Essays for Senior High School Students in Taiwan
... A, features about pronunciation were normalized by the number of words; the feature about the distribution of the number of lexical ambiguities would be normalized by the number of ...with features in Group ... See full document
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Determining Case in Arabic: Learning Complex Linguistic Behavior Requires Complex Linguistic Features
... In Modern Standard Arabic (MSA), all nouns and adjectives have one of three cases: nominative (N OM ), accusative (A CC ), or genitive (G EN ). What sets case in MSA apart from case in other languages is most saliently ... See full document
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Feature Based Selection of Dependency Paths in Ad Hoc Information Retrieval
... tested using three TREC collections: Robust04, WT10G, and ...a diverse platform for ...machine learning technique for catenae selection that captures both key aspects of heuristic filters, and novel ... See full document
10
Error Detection Using Linguistic Features
... of linguistic features into error de- tection: lexical features of words, and syn- tactic features from a robust lexicalized ...Transformation-based learning is chosen to predict ... See full document
8
Learning Predictive Linguistic Features for Alzheimer’s Disease and related Dementias using Verbal Utterances
... that using ML algorithms by learning syntactic and lexical features from the verbal utterances of elderly people can help the di- agnosis of Alzheimers and the related Dementia ...representative ... See full document
10
Exploring Measures of “Readability” for Spoken Language: Analyzing linguistic features of subtitles to identify age specific TV programs
... The experiments showed that our linguistically motivated features perform very well, achieving a classification accuracy of 95.9% (section 4.2). Apart from the entire feature set, we also exper- imented with small ... See full document
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Modeling Comma Placement in Chinese Text for Better Readability using Linguistic Features and Gaze Information
... the linguistic features of text and the gaze features of ...obstructing readability, by comparing the gaze features of persons reading text with and without ... See full document
10
Cognitively Motivated Features for Readability Assessment
... with ID, we asked participants what they enjoyed learning or reading about. The majority of our subjects mentioned enjoying watching the news, in particular local news. Many mentioned they were interested in ... See full document
9
Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal Salmonella
... machine learning models for predicting MICs for 15 ...selecting diverse genomes for the training sets, we show that highly accurate MIC prediction models can be generated with less than 500 ...Finally, ... See full document
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Assessing the Readability of Sentences: Which Corpora and Features?
... of features belonging to individual levels of linguistic description in predicting text ...of features and compared the results of different statistical classi- fiers trained on different classes of ... See full document
11
Spoken Text Difficulty Estimation Using Linguistic Features
... many linguistic features such as vocabulary and grammar, efforts were made to apply readability formula to the difficulty of spoken texts, rending promising results that the listenabil- ity of spoken ... See full document
10
Influence of Target Reader Background and Text Features on Text Readability in Bangla: A Computational Approach
... to predict reading difficulty of a Bangla document perceived according to different target reader ...text features on text comprehensibility in ...analyze readability of text documents based on the ... See full document
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NLP–Based Readability Assessment of Health–Related Texts: a Case Study on Italian Informed Consent Forms
... predefined readability classes: this is the case, for instance, of Kauchak et ...machine learning classifier for predicting the difficulty of medical texts trained on a data set of aligned sentence pairs ... See full document
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Are Cohesive Features Relevant for Text Readability Evaluation?
... additional features such as the proportion of specific deictic pronouns (such as en,y) (58), the proportion of adverbs as mentions (59), the resumptive pronouns (60), complex men- tions (including groups or ... See full document
11
Features Indicating Readability in Swedish Text
... We use the Waikato Environment for Knowledge Analysis (Weka) suite and its implementation of the popular classification algorithm Support Vector Machine (SVM). Support Vector Machines has been increasingly popular in ... See full document
14
Using Deep Linguistic Features for Finding Deceptive Opinion Spam
... Our work uses gold-standard dataset collected by (Ott et al., 2011) and non-gold standard Chinese dataset collected by ourselves. We give a machine learning model which is about 2 percent better than previous ... See full document
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Perceived audio quality of sounds degraded by non linear distortions and single ended assessment using HASQI
... and predict perceived degradation in speech, music, and soundscapes has been ...gauged using HASQI (Hearing Aid Sound Quality ...correctly predict HASQI from distorted samples to an accuracy of ... See full document
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Error Detection for Statistical Machine Translation Using Linguistic Features
... The extracted linguistic features are quite com- pact, which can be learned from a small train- ing set. Furthermore, The learned linguistic fea- tures are system-independent. Therefore our ap- ... See full document
8
The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach
... Ciaramita et al. (2005) studied the effects of do- main adaptation on NER using two distinct datasets for training and testing. They trained several NER classifiers on the CONLL 2003 dataset and evalu- ated them ... See full document
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