Papers with OntoNotes
Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition (2021.acl-short)
Copied to clipboard
| Challenge: | Existing approaches to Chinese Named Entity Recognition (NER) lack explicit word boundary and tenses information. |
| Approach: | They propose a boundary enhanced approach for Chinese Named Entity Recognition . they add an additional Graph Attention Network(GAT) layer to capture internal dependency of phrases . |
| Outcome: | The proposed approach improves Chinese Named Entity Recognition (NER) on OntoNotes and Weibo corpora. |
An Annotated Dataset of Coreference in English Literature (2020.lrec-1)
Copied to clipboard
| Challenge: | Using OntoNotes, coreference resolution systems are typically evaluated on this data exclusively. |
| Approach: | They present a new dataset of coreference annotations for works of literature in English covering 29,103 mentions in 210,532 tokens from 100 works of fiction published between 1719 and 1922. |
| Outcome: | The proposed dataset covers 29,103 mentions in 210,532 tokens from 100 works of fiction published between 1719 and 1922. |
PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution (D18-1)
Copied to clipboard
| Challenge: | Existing methods for coreference resolution are based on word2vec-like representations of entities. |
| Approach: | They propose a large-scale English dataset for coreference resolution . they use 38K documents and 12.5M words from English-speaking preschoolers . |
| Outcome: | The proposed dataset is more efficient with higher training-test overlap than OntoNotes . the study also shows that mention detection and clustering are more efficient on PreCo . |
Reliability-aware Dynamic Feature Composition for Name Tagging (P19-1)
Copied to clipboard
| Challenge: | Word embeddings are used to encode semantic information, but their quality is not consistent across the vocabulary due to the long-tail distribution of word frequency. |
| Approach: | They propose a reliability-aware name tagging model that uses word frequency to indicate word quality . they propose to use word frequency-based reliability signals to dynamically select and compose features . |
| Outcome: | The proposed model outperforms the baseline model on OntoNotes 5.0 and up to 5% gain on cross-genre data sets. |
Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution (2024.naacl-short)
Copied to clipboard
| Challenge: | Current end-to-end coreference resolution models combine detection of singleton mentions and antecedent linking into a single step. |
| Approach: | They add a singleton detector to a coarse-to-fine coreference model and design an anaphoricity-aware span embedding and singletont detection loss. |
| Outcome: | The proposed method significantly improves model performance on OntoNotes and four additional datasets. |
Sentence-Incremental Neural Coreference Resolution (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing systems for identifying spans as mentions are based on mention-pair formulations, but they are not generalized beyond pronouns. |
| Approach: | They propose a sentence-incremental neural coreference resolution system which incrementally builds clusters after marking mention boundaries in a shift-reduce method. |
| Outcome: | The proposed system outperforms state-of-the-art methods on OntoNotes and CODI-CRAC 2021 datasets and is comparable to state- of-the art methods. |
Online Coreference Resolution for Dialogue Processing: Improving Mention-Linking on Real-Time Conversations (2022.starsem-1)
Copied to clipboard
| Challenge: | Existing models for online decoding on active input are not trained to handle an online decode environment. |
| Approach: | They propose a new direction of coreference resolution for online decoding on actively generated input such as dialogue . they propose to use models that accept utterances and their past context and find mentions upon each dialogue turn . |
| Outcome: | The proposed model outperforms the baseline model by 10% on three datasets: Friends, OntoNotes, and BOLT. |
Annotating Temporally-Anchored Spatial Knowledge by Leveraging Syntactic Dependencies (L18-1)
Copied to clipboard
| Challenge: | Existing approaches to extract spatial knowledge focus on extracting locations of events, someone or something. |
| Approach: | They propose a method to annotate temporally-anchored spatial knowledge on top of OntoNotes by crowdsourcing annotations. |
| Outcome: | The proposed method can be automated and validated using syntactic dependencies and crowdsourced annotations. |
BASHI: A Corpus of Wall Street Journal Articles Annotated with Bridging Links (L18-1)
Copied to clipboard
| Challenge: | Bridging resolution is an under-researched area of NLP where the lack of annotated training data makes the application of statistical models difficult. |
| Approach: | They propose to use a corpus resource for the anaphoric phenomenon of bridging to add briding anamorphs to other gold annotations created as part of the OntoNotes project. |
| Outcome: | The proposed corpus adds bridging anaphors and their antecedents to other gold annotations created as part of the OntoNotes project. |
Why Aren’t We NER Yet? Artifacts of ASR Errors in Named Entity Recognition in Spontaneous Speech Transcripts (2023.acl-long)
Copied to clipboard
Piotr Szymański, Lukasz Augustyniak, Mikolaj Morzy, Adrian Szymczak, Krzysztof Surdyk, Piotr Żelasko
| Challenge: | despite advances in language models, the transcript of spontaneous human-human conversations remains an insurmountable challenge for most models. |
| Approach: | They examine the relationship between ASR and NER errors which limit NER models' ability to recover entity mentions from spontaneous speech transcripts. |
| Outcome: | The proposed model fails even if no word errors are introduced by the ASR . the proposed model's performance deteriorates when applied to the ASL outputs . |
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification (2021.acl-long)
Copied to clipboard
| Challenge: | Named entity recognition and classification (NERC) tasks require annotated data for the target classes during training. |
| Approach: | They propose a novel approach that leverages the fact that textual descriptions for many entity classes occur naturally. |
| Outcome: | The proposed approach outperforms baselines adapted from machine reading comprehension and zero-shot text classification. |
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition (2022.findings-acl)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) systems perform well on in-distribution data, but perform poorly on examples drawn from a shifted distribution. |
| Approach: | They propose to use expert-guided heuristics to change entity tokens and their contexts to alter their entity types as adversarial attacks. |
| Outcome: | The proposed model significantly improves performance on the challenging set and out-of-domain generalization. |
Exploiting Hierarchically Structured Categories in Fine-grained Chinese Named Entity Recognition (2023.findings-acl)
Copied to clipboard
| Challenge: | Named Entity Recognition (CNER) is a widely used technology in various applications. |
| Approach: | They propose a method that uses a custom-designed relevance scoring function to learn the potential relevance between different flattened hierarchical labels. |
| Outcome: | The proposed method outperforms the state-of-the-art on the FiNE dataset. |
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing (2022.coling-1)
Copied to clipboard
| Challenge: | Experimental results show that fine-grained entity typing (FET) can be used to deduce specific semantic types of entities. |
| Approach: | They propose a type-enriched hierarchical contrastive strategy to model type differences . their method can make type information directly perceptible and improve distinguishability . |
| Outcome: | The proposed method can model the differences between hierarchical types and distinguish multi-grained similar types at different granularities. |
Adapting Coreference Resolution to Twitter Conversations (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on coreference resolution for Twitter texts show that performance is low. |
| Approach: | They propose to use Twitter conversations to train a system that is originally trained on OntoNotes to improve coreference resolution. |
| Outcome: | The proposed system outperforms existing systems on Twitter by 21.6%. |
Unsupervised Word Polysemy Quantification with Multiresolution Grids of Contextual Embeddings (2021.eacl-main)
Copied to clipboard
| Challenge: | a new method to quantify polysemy is based on basic geometry in the contextual embedding space . word sense annotation has always been one of the tasks with the lowest interannotator agreement . |
| Approach: | They propose a method to estimate polysemy based on simple geometry in contextual embedding space. |
| Outcome: | The proposed method is fully unsupervised and data-driven . it can be used to sample sentences with different senses at no extra cost . |
Moving on from OntoNotes: Coreference Resolution Model Transfer (2021.emnlp-main)
Copied to clipboard
| Challenge: | Academic neural models for coreference resolution (coref) are typically trained on OntoNotes and model improvements are benchmarked on that dataset. |
| Approach: | They aim to quantify transferability of coref models based on the number of annotated documents available in the target dataset. |
| Outcome: | The proposed model improvements are consistent with the state-of-the-art results on PreCo. |
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for Named Entity Recognition only learn class-specific semantic features and intermediate representations from source domains, resulting in suboptimal performance. |
| Approach: | They propose a contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER. |
| Outcome: | The proposed technique outperforms existing methods by 3%-13% absolute F1 points while showing consistent performance trends. |
BERT for Coreference Resolution: Baselines and Analysis (D19-1)
Copied to clipboard
| Challenge: | Recent BERT-based models have reported dramatic gains on multiple semantic benchmarks including question-answering, natural language inference, and named entity recognition. |
| Approach: | They apply BERT to coreference resolution, achieving a new state of the art on the GAP and OntoNotes benchmarks. |
| Outcome: | A qualitative analysis of model predictions shows that BERT-large is better at distinguishing between related but distinct entities, but there is room for improvement in modeling document-level context, conversations, and mention paraphrasing. |
Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks (2020.emnlp-main)
Copied to clipboard
| Challenge: | Current models for document coreference resolution have large memory requirements and quadratic runtime in document length. |
| Approach: | They propose a memory-augmented neural network that tracks only a small number of entities at a time. |
| Outcome: | The proposed model outperforms existing models on OntoNotes and LitBank in memory management and memory management. |
SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing attempts to integrate singleton mention detection into end-to-end coreference resolution for English have been hampered by the lack of singletont mention spans in the OntoNotes benchmark. |
| Approach: | They propose a two-step neural mention and coreference resolution system that integrates singleton mentions with OntoNotes syntax trees to achieve a near approximation of the Ontonotes dataset with all singletont mentions. |
| Outcome: | The proposed system achieves 94% recall on a sample of gold singletons. |
Interpretable Coreference Resolution Evaluation Using Explicit Semantics (2026.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation methods for coreference resolution are limited by semantic and contextual information. |
| Approach: | They propose a semantically-enhanced evaluation framework for coreference resolution that assigns semantic labels to nominal mentions and propagates them to entire coreference clusters. |
| Outcome: | The proposed framework uncovers systematic weaknesses obscured by standard metrics. |