A Machine Learning Approach to Pronominal Anaphora Resolution in Dialogue Based Intelligent Tutoring Systems

Niraula, Nobal B.; Rus, Vasile

doi:10.1007/978-3-642-54906-9_25

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…This feature gained huge attention [26], Stent and Bangalore [25] worked on a specific conversation-based dataset to improve the relative performance. A detailed study of the turn structures is discussed in detail by [33], using a specific dataset of dialogues on the tutoring system. The procedure considered the location information of the candidate antecedent, to analyze the corpus.…”

Section: Identification Of Subjective Wordsmentioning

confidence: 99%

“…The primary aim of the proposed approach can be termed as an antecedent scoring system that tries to search for a suitable noun phrase for a given pronoun that appeared in the candidate reply tweet. Though there are few pronoun resolution systems, such as [23], [33]- [35], that try to resolve the same through the contextual properties, and even fewer articles that use platform-specific structural properties such as [36], to the best of our knowledge there is no proposal in literature till date that try to use both structural and contextual properties for pronoun resolution for streaming data from social medial. Therefore, in our proposal, we try to employ the structural properties that a social media platform provides, along with contextual properties, similar to [23], of the related sentences to achieve our aim.…”

Section: Antecedent Scoring Systemmentioning

confidence: 99%

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Meliorating usable document density for online event detection

Samanta

Meena²,

Mazumdar³

et al. 2022

IJ-ICT

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<span>Online event detection (OED) has seen a rise in the research community as it can provide quick identification of possible events happening at times in the world. Through these systems, potential events can be indicated well before they are reported by the news media, by grouping similar documents shared over social media by users. Most OED systems use textual similarities for this purpose. Similar documents, that may indicate a potential event, are further strengthened by the replies made by other users, thereby improving the potentiality of the group. However, these documents are at times unusable as independent documents, as they may replace previously appeared noun phrases with pronouns, leading OED systems to fail while grouping these replies to their suitable clusters. In this paper, a pronoun resolution system that tries to replace pronouns with relevant nouns over social media data is proposed. Results show significant improvement in performance using the proposed system.</span>

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Section: Identification Of Subjective Wordsmentioning

confidence: 99%

Section: Antecedent Scoring Systemmentioning

confidence: 99%

Meliorating usable document density for online event detection

Samanta

Meena²,

Mazumdar³

et al. 2022

IJ-ICT

View full text Add to dashboard Cite

show abstract

“…Past work in creation of large publicly-available datasets of human-to-human tutoring interactions has been limited. Relevant past work which utilizes tutoring dialogue datasets draws from proprietary data collections (Chen et al, 2019;Rus et al, 2015a) or dialogues gathered from a student's interactions with an automated tutor (Niraula et al, 2014;Forbes-Riley and Litman, 2013).…”

Section: Tutoring Dialogue Corpus Creationmentioning

confidence: 99%

CIMA: A Large Open Access Dialogue Dataset for Tutoring

Stasaski

Kao²,

Hearst

2020

Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

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One-to-one tutoring is often an effective means to help students learn, and recent experiments with neural conversation systems are promising. However, large open datasets of tutoring conversations are lacking. To remedy this, we propose a novel asynchronous method for collecting tutoring dialogue via crowdworkers that is both amenable to the needs of deep learning algorithms and reflective of pedagogical concerns. In this approach, extended conversations are obtained between crowdworkers role-playing as both students and tutors. The CIMA collection, which we make publicly available, is novel in that students are exposed to overlapping grounded concepts between exercises and multiple relevant tutoring responses are collected for the same input.CIMA contains several compelling properties from an educational perspective: student roleplayers complete exercises in fewer turns during the course of the conversation and tutor players adopt strategies that conform with some educational conversational norms, such as providing hints versus asking questions in appropriate contexts. The dataset enables a model to be trained to generate the next tutoring utterance in a conversation, conditioned on a provided action strategy.

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Using Neural Tensor Networks for Open Ended Short Answer Assessment

Gautam

Rus

2020

Lecture Notes in Computer Science

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A Machine Learning Approach to Pronominal Anaphora Resolution in Dialogue Based Intelligent Tutoring Systems

Cited by 8 publications

References 13 publications

Meliorating usable document density for online event detection

Meliorating usable document density for online event detection

CIMA: A Large Open Access Dialogue Dataset for Tutoring

Using Neural Tensor Networks for Open Ended Short Answer Assessment

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