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Andreea Dutulescu; Stefan Ruseti; Denis Iorga; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
The process of generating challenging and appropriate distractors for multiple-choice questions is a complex and time-consuming task. Existing methods for an automated generation have limitations in proposing challenging distractors, or they fail to effectively filter out incorrect choices that closely resemble the correct answer, share synonymous…
Descriptors: Multiple Choice Tests, Artificial Intelligence, Attention, Natural Language Processing
Corlatescu, Dragos-Georgian; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2021
Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of…
Descriptors: Reading Comprehension, Reading Processes, Memory, Schemata (Cognition)
Corlatescu, Dragos-Georgian; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2021
Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of…
Descriptors: Reading Comprehension, Memory, Inferences, Syntax
Nicula, Bogdan; Perret, Cecile A.; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2020
Open-ended comprehension questions are a common type of assessment used to evaluate how well students understand one of multiple documents. Our aim is to use natural language processing (NLP) to infer the level and type of inferencing within readers' answers to comprehension questions using linguistic and semantic features within their responses.…
Descriptors: Natural Language Processing, Taxonomy, Responses, Semantics
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Magliano, Joseph P.; Lampi, Jodi P.; Ray, Melissa; Chan, Greta – Grantee Submission, 2020
Coherent mental models for successful comprehension require inferences that establish semantic "bridges" between discourse constituents and "elaborations" that incorporate relevant background knowledge. While it is established that individual differences in the extent to which postsecondary students engage in these processes…
Descriptors: Reading Comprehension, Reading Strategies, Inferences, Reading Tests
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Keezhatta, Muhammed Salim – Arab World English Journal, 2019
Natural Language Processing (NLP) platforms have recently reported a higher adoption rate of Artificial Intelligence (AI) applications. The purpose of this research is to examine the relationship between NLP and AI in the application of linguistic tasks related to morphology, parsing, and semantics. To achieve this objective, a theoretical…
Descriptors: Models, Correlation, Natural Language Processing, Artificial Intelligence
Sharp, Rebecca Reynolds – ProQuest LLC, 2017
We address the challenging task of "computational natural language inference," by which we mean bridging two or more natural language texts while also providing an explanation of how they are connected. In the context of question answering (i.e., finding short answers to natural language questions), this inference connects the question…
Descriptors: Computation, Natural Language Processing, Inferences, Questioning Techniques
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Allen, Laura K.; Perret, Cecile; McNamara, Danielle S. – Grantee Submission, 2016
The relationship between working memory capacity and writing ability was examined via a linguistic analysis of student essays. Undergraduate students (n = 108) wrote timed, prompt-based essays and completed a battery of cognitive assessments. The surface- and discourse-level linguistic features of students' essays were then analyzed using natural…
Descriptors: Cognitive Processes, Writing (Composition), Short Term Memory, Writing Ability
Johnson, Benjamin Luke – ProQuest LLC, 2013
Much of the world's knowledge is encoded in natural language. Accessing this information would be invaluable for applications such as agent systems, question answering, the semantic web, expert systems, and many more. However, language is very ambiguous--each word in a natural language utterance can have a variety of meanings. Word sense…
Descriptors: Natural Language Processing, Ambiguity (Semantics), Inferences, Semantics
Pon-Barry, Heather Roberta – ProQuest LLC, 2013
The field of spoken language processing is concerned with creating computer programs that can understand human speech and produce human-like speech. Regarding the problem of understanding human speech, there is currently growing interest in moving beyond speech recognition (the task of transcribing the words in an audio stream) and towards…
Descriptors: Speech Communication, Inferences, Natural Language Processing, Psychological Patterns
Lee, Chong Min – ProQuest LLC, 2013
This dissertation investigates the temporal relation identification task. The goal is to construct consistent temporal relations between temporal entities (e.g., events and time expressions) in a narrative. Constructing consistent temporal relations is challenging due to the exponential increase in the number of candidates for temporal relations…
Descriptors: Natural Language Processing, Computational Linguistics, Task Analysis, Time Perspective
Smith, David Arthur – ProQuest LLC, 2010
Much recent work in natural language processing treats linguistic analysis as an inference problem over graphs. This development opens up useful connections between machine learning, graph theory, and linguistics. The first part of this dissertation formulates syntactic dependency parsing as a dynamic Markov random field with the novel…
Descriptors: Semantics, Syntax, Bilingualism, Monolingualism
Tu, Yuancheng – ProQuest LLC, 2012
The fundamental problem faced by automatic text understanding in Natural Language Processing (NLP) is to identify semantically related pieces of text and integrate them together to compute the meaning of the whole text. However, the principle of compositionality runs into trouble very quickly when real language is examined with its frequent…
Descriptors: English, Verbs, Computational Linguistics, Natural Language Processing
Boyd-Graber, Jordan – ProQuest LLC, 2010
Topic models like latent Dirichlet allocation (LDA) provide a framework for analyzing large datasets where observations are collected into groups. Although topic modeling has been fruitfully applied to problems social science, biology, and computer vision, it has been most widely used to model datasets where documents are modeled as exchangeable…
Descriptors: Language Patterns, Semantics, Linguistics, Multilingualism
Do, Quang Xuan – ProQuest LLC, 2012
In this thesis, we study the importance of background knowledge in relation extraction systems. We not only demonstrate the benefits of leveraging background knowledge to improve the systems' performance but also propose a principled framework that allows one to effectively incorporate knowledge into statistical machine learning models for…
Descriptors: Prior Learning, Natural Language Processing, Information Retrieval, Computer Science
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