Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions
Word embeddings capture semantic meaning of individual words. How to bridge word-level linguistic knowledge with sentence-level language representation is an open problem. This paper examines whether sentence-level representations can be achieved by building a custom sentence database focusing on one aspect of a sentence’s meaning. Our three separate semantic aspects are whether the sentence: (1) communicates a causal relationship, (2) indicates that two things are correlated with each other, and (3) expresses information or knowledge. The three classifiers provide epistemic information about a sentence’s content.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationSentencetext-classificationText ClassificationWord EmbeddingsSimilar Papers 제목 키워드 기반
Identifying Where to Focus in Reading Comprehension for Neural Question Generation
A first step in the task of automatically generating questions for testing reading comprehension is to identify \textit{question-worthy} sentences, i.e. sentences in a text passage that humans find it worthwhile to ask q…
Dependency ParsingMachine TranslationNamed Entity Recognition (NER)Question Generation+6Gaze-Driven Sentence Simplification for Language Learners: Enhancing Comprehension and Readability
Language learners should regularly engage in reading challenging materials as part of their study routine. Nevertheless, constantly referring to dictionaries is time-consuming and distracting. This paper presents a novel…
Reading ComprehensionSentenceDIFM:An effective deep interaction and fusion model for sentence matching
“Natural language sentence matching is the task of comparing two sentences and identifying the relationship between them. It has a wide range of applications in natural language processing tasks such as reading comprehen…
Machine Reading ComprehensionNatural Language InferenceReading ComprehensionSentenceAligning Sentence Simplification with ESL Learner's Proficiency for Language Acquisition
Text simplification is crucial for improving accessibility and comprehension for English as a Second Language (ESL) learners. This study goes a step further and aims to facilitate ESL learners' language acquisition by si…
DiversityLanguage AcquisitionLanguage ModelingLanguage Modelling+3Power in Numbers: Robust reading comprehension by finetuning with four adversarial sentences per example
Recent models have achieved human level performance on the Stanford Question Answering Dataset when using F1 scores to evaluate the reading comprehension task. Yet, teaching machines to comprehend text has not been solve…
Question AnsweringReading ComprehensionSentence