Papers with textual mentions
Exploiting Structure in Representation of Named Entities using Active Learning (C18-1)
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| Challenge: | Named entities are atomic objects of reference and reasoning in many knowledge-centric applications. |
| Approach: | They propose an active-learning based framework that drastically reduces the labeled data required to learn entities' structures. |
| Outcome: | The proposed framework outperforms handwritten programs and supervised learning models in relation extraction and entity resolution tasks. |
Improving Entity Linking by Modeling Latent Relations between Mentions (P18-1)
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| Challenge: | Entity linking systems often exploit relations between textual mentions to decide if the linking decisions are compatible. |
| Approach: | They treat relations as latent variables while optimizing the neural entity-linking model without supervision. |
| Outcome: | The proposed model outperforms its relation-agnostic version and significantly outperformed its relational version. |
Learning Relational Representations by Analogy using Hierarchical Siamese Networks (N19-1)
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| Challenge: | Existing approaches to learn representations of relations by textual mentions require a large amount of examples for each relation to reach satisfactory performance. |
| Approach: | They propose a method to learn representations of relations expressed by their textual mentions by matching triples in knowledge bases with web-scale corpora through distant supervision. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on a relation extraction task. |
Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings (2025.naacl-long)
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| Challenge: | Existing methods to analyze online reviews for aspects of quality are limited . authors propose a method to disentangle the impact of each aspect on overall perception . |
| Approach: | They propose a method to disentangle the effect of each aspect on overall perception . they use textual mentions in reviews as proxies for real-world attributes . |
| Outcome: | The proposed method improves on real-world reviews of U.S. K-12 schools. |
A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Semi-inductive link prediction (LP) is a task of predicting facts for new, previously unseen entities based on context information. |
| Approach: | They propose to use Wikidata5M to evaluate semi-inductive link prediction (LP) in knowledge graphs. |
| Outcome: | The proposed benchmark provides a test bed for further research into semi-inductive link prediction (LP) in knowledge graphs. |