Learning by Reading by Learning to Read

Bethard, Steven; Martin, James H.; Klingenstein, Sara

doi:10.1109/icsc.2007.101

Cited by 33 publications

(6 citation statements)

References 23 publications

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“…MLP layer consists of |H| * |K| + |K| * |K| parameters 8 PyTorch code will be made available upon acceptance. annotated temporal relation corpora with all events and relations fully annotated is reported to be a challenging task as annotators could easily overlook some facts (Bethard et al, 2007;Ning et al, 2017), which made both modeling and evaluation extremely difficult in previous event temporal relation research. The TB-Dense dataset mitigates this issue by forcing annotators to examine all pairs of events within the same or neighboring sentences, and it has been widely evaluated on this task Ning et al, 2017;Cheng and Miyao, 2017;Meng and Rumshisky, 2018).…”

Section: Temporal Relation Datamentioning

confidence: 99%

“…Temporal relation corpora such as TimeBank (Pustejovsky et al, 2003) and RED (O'Gorman et al, 2016) annotated temporal relation corpora with all events and relations fully annotated is reported to be a challenging task as annotators could easily overlook some facts (Bethard et al, 2007;Ning et al, 2017), which made both modeling and evaluation extremely difficult in previous event temporal relation research.…”

Section: Temporal Relation Datamentioning

confidence: 99%

See 1 more Smart Citation

Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

Han¹,

Ning²,

Peng³

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. The proposed method has two advantages over existing work. First, it improves event representation by allowing the event and relation modules to share the same contextualized embeddings and neural representation learner. Second, it avoids error propagation in the conventional pipeline systems by leveraging structured inference and learning methods to assign both the event labels and the temporal relation labels jointly. Experiments show that the proposed method can improve both event extraction and temporal relation extraction over state-of-the-art systems, with the end-to-end F 1 improved by 10% and 6.8% on two benchmark datasets respectively.

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Section: Temporal Relation Datamentioning

confidence: 99%

Section: Temporal Relation Datamentioning

confidence: 99%

Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

Han¹,

Ning²,

Peng³

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…The common issue of these corpora is missing annotations. Collecting densely annotated temporal relation corpora with all events and relations fully annotated is a challenging task as annotators could easily overlook some facts (Bethard et al, 2007;Ning et al, 2017). Table 2: Overall experiment results: per MacNemar's test, the improvements against the end-to-end baseline models by adding inference with distributional constraints are both statistically significant for TimeBank-Dense (p-value < 0.005) and I2B2-TEMPORAL (p-value < 0.0005).…”

Section: Datamentioning

confidence: 99%

“…The common issue in these corpora is missing annotation. Collecting densely annotated temporal relation corpora with all event pairs fully annotated has been reported to be a challenging task as annotators could easily overlook some pairs Bethard et al, 2007;. TB-Dense dataset mitigates this issue by forcing annotators to examine all pairs of events within the same or neighboring sentences.…”

Section: Related Workmentioning

confidence: 99%

Deep Structured Neural Network for Event Temporal Relation Extraction

Han¹,

Hsu²,

Yang³

et al. 2019

Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

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We propose a novel deep structured learning framework for event temporal relation extraction. The model consists of 1) a recurrent neural network (RNN) to learn scoring functions for pair-wise relations, and 2) a structured support vector machine (SSVM) to make joint predictions. The neural network automatically learns representations that account for long-term contexts to provide robust features for the structured model, while the SSVM incorporates domain knowledge such as transitive closure of temporal relations as constraints to make better globally consistent decisions. By jointly training the two components, our model combines the benefits of both data-driven learning and knowledge exploitation. Experimental results on three highquality event temporal relation datasets (TCR, MATRES, and TB-Dense) demonstrate that incorporated with pre-trained contextualized embeddings, the proposed model achieves significantly better performances than the stateof-the-art methods on all three datasets. We also provide thorough ablation studies to investigate our model.

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“…The rest of the algorithm treats cases in which one or both nominal elements is not known. Processing can optionally involve machine learning using bootstrapping techniques with which we have experimented (e.g., Nirenburg et al 2007). Although our experimentation is in the early stages, we believe machine learning methods could have high payoff potential, both for semi-automatic knowledge acquisition prior to system runs and for just-in-time support for processing unrecorded lexical stock.…”

Section: Examples Of Connected Constraintsmentioning

confidence: 99%

Nominal Compound Interpretation by Intelligent Agents

McShane

Beale

Babkin

2014

LiLT

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This paper presents a cognitively-inspired algorithm for the semantic analysis of nominal compounds by intelligent agents. The agents, modeled within the OntoAgent environment, are tasked to compute a full context-sensitive semantic interpretation of each compound using a battery of engines that rely on a high-quality computational lexicon and ontology. Rather than being treated as an isolated “task”, as in many NLP approaches, nominal compound analysis in OntoAgent represents a minimal extension to the core process of semantic analysis. We hypothesize that seeking similarities across language analysis tasks reflects the spirit of how people approach language interpretation, and that this approach will make feasible the long-term development of truly sophisticated, human-like intelligent agents. The initial evaluation of our approach to nominal compounds are fixed expressions, requiring individual semantic specification at the lexical level.

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Learning by Reading by Learning to Read

Cited by 33 publications

References 23 publications

Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

Deep Structured Neural Network for Event Temporal Relation Extraction

Nominal Compound Interpretation by Intelligent Agents

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