Papers with segmentation task

5 papers
Extraction of the Argument Structure of Tokyo Metropolitan Assembly Minutes: Segmentation of Question-and-Answer Sets (2020.lrec-1)

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Challenge: a study analyzed local assembly minutes in Japan using a unified format . local assembly minute data is expensive to analyze because of the different ways they are released to the public.
Approach: They construct a corpus of Japanese local assembly minutes based on local autonomy law . they structured all statements in assembly minutes and extracted question and answer pairs .
Outcome: The results show that the minutes are the primary information for local politics.
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit (D18-1)

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Challenge: a structured prediction framework is proposed to solve word segmentation and morphological tagging tasks in a free word order language.
Approach: They propose a structured prediction framework that jointly solves word segmentation and morphological tagging tasks in Sanskrit.
Outcome: The proposed model outperforms the state of the art with an F-Score of 96.92 (percentage improvement of 7.06%) while using less than one tenth of the task-specific training data.
Word Segmentation as Unsupervised Constituency Parsing (2022.acl-long)

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Challenge: Existing theories of word identification from continuous inputs are based on statistical cues, such as Bayesian inference and normative statistics.
Approach: They propose a model which allows for a process isomorphic to unsupervised constituency parsing and which can reproduce human behavior in word identification experiments.
Outcome: The proposed model reproduces human behavior in word identification experiments, suggesting it is viable to study word identification and its relation to syntactic processing.
Assessing the State of the Art in Scene Segmentation (2025.naacl-long)

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Challenge: Recent advances in scene segmentation have made it difficult to detect scenes in literary texts.
Approach: They propose to modify existing models to improve detection of scenes in literary texts . they propose to use a training sample generation scheme to alleviate this problem .
Outcome: The proposed model is more robust to different types of texts, while its overall performance is slightly worse than that of BERT-based models.
A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks (L18-1)

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Challenge: Phonetic segmentation is the process of splitting speech into distinct phonetic units . methods for automatic segmentation are not always accurate enough .
Approach: They propose to model phonetic segmentation as close as possible to manual segmentation by recording experts performing a segmentation task.
Outcome: This corpus captures human segmentation behavior by recording experts performing a segmentation task.

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