[PDF] Top 20 Word Embedding based Content Features for Automated Oral Proficiency Scoring
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Word Embedding based Content Features for Automated Oral Proficiency Scoring
... develop content features for an automated scoring system of non-native En- glish speakers’ spontaneous ...The features calculate the lexical similarity between the question text and the ... See full document
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Speech and Text driven Features for Automated Scoring of English Speaking Tasks
... speech- based and content-based features should outper- form models using only one of them, it may not turn out that ...new features measuring vocabulary, gram- mar or content ... See full document
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Content Linking for UGC based on Word Embedding Model
... that word vectors may sometimes lead to "excessive ...the word vectors can not only help to link two relevant sentences without the same vocabulary but also wrongly link two unrelated sentences by ... See full document
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Automated essay scoring with string kernels and word embeddings
... n-gram features) and word embeddings (high-level semantic features) to obtain state-of-the-art AES ...methods based on string kernels have demonstrated remarkable performance in various text ... See full document
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Automated Scoring of Picture based Story Narration
... construct features for awkward word usage and content rele- vance for a written vocabulary test which we adapt for our ...organization features have been employed for essay scoring of ... See full document
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Prompt based Content Scoring for Automated Spoken Language Assessment
... of content scoring features based solely on the prompt stimu- lus materials and a sample response is a viable al- ternative to using features based on content mod- els ... See full document
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Automated Content Scoring of Spoken Responses in an Assessment for Teachers of English
... the content correctness of spoken responses in a new language test for teachers of English as a foreign language who are non-native speakers of ...spoken proficiency elic- it responses that are either very ... See full document
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Content Modeling for Automated Oral Proficiency Scoring System
... All responses were scored by the trained raters using a 4-point scoring scale from 1 to 4 with 4 indicating the highest proficiency. In addition, raters provided a score of 0 when test takers did not show ... See full document
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Combining elicited imitation and fluency features for oral proficiency measurement
... automatic oral testing method is referred to as semi-direct or simulated speech ...test. Automated scoring (Bernstein et ...or word-spotting, or ex- traction of specific fluency-related ... See full document
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Exploring Content Features for Automated Speech Scoring
... score between two documents is then calculated by combining the similarity of the words they contain, weighted by their word specificity (i.e., IDF values). In this paper, we use these three similarity mea- sures ... See full document
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Using collocational features to improve automated scoring of EFL texts
... the bigrams present in an EFL text two associa- tion scores (ASs), computed on the basis of a large native reference corpus: (pointwise) Mutual Infor- mation (MI), which favours bigrams made up of low-frequency words, ... See full document
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Semi Supervised Neural System for Tagging, Parsing and Lematization
... This paper describes the ICS PAS system which took part in CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. The sys- tem consists of jointly trained tagger, lem- matizer, and ... See full document
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Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums
... From Table 3, we can find out that, for Method 1, the bigger threshold usually can bring the higher precision. But the sentences we obtain may be fewer, too. This will cause low recall rate. Ac- cording to the precision ... See full document
5
Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring
... Keywords: medical image classification; local binary patterns; characteristic curves; whole slide 23.. image processing; automated HER2 scoring 24.[r] ... See full document
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Effect of Fine Grained Lexical Rating on L2 Learners’ Lexical Learning Gain
... inaccurate word meanings for target word forms; likewise, in taking a productive vocabulary test, learners may generate inaccurate orthographic (or phonological) word ...initial word learning ... See full document
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Automated scoring across different modalities
... the scoring engine for this response. We first hypothesized that the scoring error may be greater for responses with higher ...the scoring error (the absolute difference between pre- dicted and ... See full document
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Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP OST 2019
... linguistic features, hybrid loss including ranking and Conditional Random Fields (CRF), multi-task objective and token- level ensembling strategy to improve ...dictionary based fuzzy and semantic search to ... See full document
11
Angular-Based Word Meta-Embedding Learning
... the embedding set into a single vector and trains the autoencoder so to produce a lower-dimensional representation (shown in red), while the decoupled autoencoder (DAEME) keeps the embedding vec- tors ... See full document
5
Beyond Context: A New Perspective for Word Embeddings
... most word embedding models is ...a word also characterizes its ...the word prediction task as a multi- label classification ...of embedding learning models, which allows different ... See full document
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Recursive Neural Conditional Random Fields for Aspect based Sentiment Analysis
... the difficulty of incorporating dependency structure explicitly as input features, which motivates the de- sign of our model to use DT-RNN to encode depen- dency between words for feature learning. The most ... See full document
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