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Hits 41 – 60 of 922

41
SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents ...
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42
Few-Shot Named Entity Recognition: An Empirical Baseline Study ...
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43
Low-resource Taxonomy Enrichment with Pretrained Language Models ...
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44
Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction ...
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45
Extend, donÕt rebuild: Phrasing conditional graph modification as autoregressive sequence labelling ...
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46
Treasures Outside Contexts: Improving Event Detection via Global Statistics ...
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47
Cost-effective End-to-end Information Extraction for Semi-structured Document Images ...
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48
Monitoring geometrical properties of word embeddings for detecting the emergence of new topics. ...
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49
Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data ...
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50
Robust Retrieval Augmented Generation for Zero-shot Slot Filling ...
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51
Zero-Shot Information Extraction as a Unified Text-to-Triple Translation ...
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52
Structure-Augmented Keyphrase Generation ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.209/ Abstract: We studied structure augmentation on documents for keyphrase generation. ...
Keyword: Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Generation
URL: https://dx.doi.org/10.48448/e0nz-8d46
https://underline.io/lecture/37549-structure-augmented-keyphrase-generation
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53
Document-level Entity-based Extraction as Template Generation ...
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54
TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network ...
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55
Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction ...
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56
Data Augmentation for Cross-Domain Named Entity Recognition ...
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57
Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction ...
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58
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection ...
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59
Active Learning by Acquiring Contrastive Examples ...
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60
A Bag of Tricks for Dialogue Summarization ...
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