Papers by Kyeongmin Rim

8 papers
Bridging the LAPPS Grid and CLARIN (L18-1)

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Challenge: The LAPPS-CLARIN project is creating a "trust network" between the Language Applications Grid and WebLicht workflow engine . the goal is to allow users on one side of the bridge to gain appropriately authenticated access to the other .
Approach: The LAPPS-CLARIN project is creating a "trust network" between the Language Applications Grid and WebLicht workflow engine hosted by the CLARIN-D Center in Tübingen.
Outcome: The LAPPS-CLARIN project is creating a "trust network" between the Language Applications (LAPPS) Grid and the WebLicht workflow engine hosted by the CLARIN-D Center in Tübingen.
Reproducing Neural Ensemble Classifier for Semantic Relation Extraction inScientific Papers (2020.lrec-1)

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Challenge: Replicability and reproducibility are core ideas of modern scientific methods.
Approach: They describe challenges encountered in reproducing the results of a top performing system in computational linguistics.
Outcome: The proposed system was able to reproduce the results of a task 7 in the domain of natural language processing and computational linguistics.
Interchange Formats for Visualization: LIF and MMIF (2020.lrec-1)

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Challenge: In this paper, we discuss the enhanced data visualization capabilities enabled by interoperating computational linguistics and natural language processing (NLP) applications.
Approach: They propose to use interchange formats to enable enhanced data visualization . they propose to combine CL tools with openly available visualization tools .
Outcome: The proposed formats can be used to create visualizations and manipulate annotations in multiple ways.
GLAMR: Augmenting AMR with GL-VerbNet Event Structure (2024.lrec-main)

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Challenge: Abstract Meaning Representation (AMR) is a general-purpose semantic encoding for language.
Approach: They propose an AMR interpretation of Generative Lexicon semantic components using a verb-net-encoded verb-node graph.
Outcome: The proposed extension is compatible with current AMR specification and can be automated.
The CLAMS Platform at Work: Processing Audiovisual Data from the American Archive of Public Broadcasting (2022.lrec-1)

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Challenge: The Computational Linguistics Applications for Multimedia Services (CLAMS) platform provides access to computational content analysis tools for multimedia material.
Approach: They describe the CLAMS platform as it is and its initial prototype implementation from 2019 . they use a common multi-modal representation language called MMIF to create a workflow .
Outcome: The CLAMS platform is a new version of an initial prototype from 2019 . it can be used to add metadata to mass-digitized multimedia collections . the proposed version is based on the American Archive of Public Broadcasting data .
Linguistically Conditioned Semantic Textual Similarity (2024.acl-long)

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Challenge: Semantic textual similarity (STS) is a fundamental NLP task that measures the semantic similarity between two sentences.
Approach: They propose to use a conditional STS dataset to measure sentences’ similarity conditioned on a certain aspect to reduce the inherent ambiguity posed by the sentences.
Outcome: The proposed method improves the performance over baselines on the C-STS dataset with over 80% F1 score.
The Coreference under Transformation Labeling Dataset: Entity Tracking in Procedural Texts Using Event Models (2023.findings-acl)

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Challenge: et al., 2023) show that entity coreference resolution is improved when events bring about changes in entities that are not reflected in text mentions.
Approach: They propose to perform transformation-based entity linking prior to coreference relation identification to improve entity coreference.
Outcome: The proposed model improves coreference resolution of entities mentioned under a process-oriented model of events.
Competence-based Question Generation (2022.coling-1)

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Challenge: Existing models of natural language understanding rely on question answering and logical inference benchmark challenges to evaluate performance of systems.
Approach: They propose a method to generate CB questions using English cooking recipes . they argue that a broader effort needs to be put on measuring linguistic competencies .
Outcome: The proposed method performs poorly on large pretrained language models until they are provided with additional contextualized semantic information.

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