Papers with conditional random fields
Hybrid semi-Markov CRF for Neural Sequence Labeling (P18-2)
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| Challenge: | Existing conditional random fields (CRFs) use hand-crafted features to perform sequence labeling tasks. |
| Approach: | They propose to use semi-Markov conditional random fields for neural sequence labeling in natural language processing to extract features from segments instead of words. |
| Outcome: | The proposed model achieves state-of-the-art when no external knowledge is used. |
Learning with Structured Representations for Negation Scope Extraction (P18-2)
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| Challenge: | Existing approaches to negation scope detection have been criticized for capturing information related to negations, long-distance dependencies and structural information. |
| Approach: | They propose to use conditional random fields, semi-Markov CRF and latent-variable CRF models to capture useful information such as long-distance dependencies and some latent structural information. |
| Outcome: | The proposed approaches can capture useful information such as features related to negation cue, long-distance dependencies and some latent structural information. |
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation (N19-1)
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| Challenge: | asynchronous domains lack large labeled datasets to train an effective speech act recognition model. |
| Approach: | They propose methods to leverage abundant unlabeled conversational data and available labeled data from synchronous domains to train an effective SAR model. |
| Outcome: | The proposed method outperforms existing methods when trained on in-domain data only. |
Ab Initio: Automatic Latin Proto-word Reconstruction (C18-1)
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| Challenge: | Existing methods for proto-word reconstruction are time-consuming and manual, but few studies have done it . a recent study used cognates to reconstruct ancient languages from their modern counterparts . |
| Approach: | They propose to use Latin proto-words to automate the process of proto-language reconstruction . they leverage information from all modern languages and use conditional random fields for sequence labeling . |
| Outcome: | The proposed method improves on previous results and requires less data . it is based on word forms in multiple Romance languages and on recurrent neural networks . |
Data Anonymization for Requirements Quality Analysis: a Reproducible Automatic Error Detection Task (L18-1)
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| Challenge: | a recent study focuses on identifying potential problems of ambiguity, completeness, conformity, singularity and readability in requirements specifications. |
| Approach: | They propose to identify potential problems of ambiguity, completeness, conformity, singularity and readability in system and software requirements specifications. |
| Outcome: | The proposed system achieves 79.47% for the F1 score on proposed evaluation data. |
NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2024.lrec-main)
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| Challenge: | Hierarchical text classification (HTC) is a challenging task in natural language processing due to its complex taxonomic label hierarchy. |
| Approach: | They propose to use prompts to model hierarchical text classification (HTC) they propose to introduce conditional random fields and Global Pointer to establish hierarchic dependencies . |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on three public datasets. |