| Challenge: | Existing knowledge bases are often based on bootstrapping entities from human-curated sources such as Wikipedia. |
| Approach: | They propose to build a knowledge base from enterprise documents with minimal human intervention by using deep learning models and classical machine learning techniques. |
| Outcome: | The proposed system is currently serving as part of a Microsoft 365 service. |
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Scalable Construction and Reasoning of Massive Knowledge Bases (N18-6)
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| Challenge: | Existing knowledge mining systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. |
| Approach: | They introduce how to extract structured facts from text corpora to construct knowledge bases. |
| Outcome: | The proposed methods are weakly-supervised and domain-independent for knowledge base construction across various domains. |
Knowledge Extraction From Texts Based on Wikidata (2022.naacl-industry)
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| Challenge: | Existing knowledge extraction pipelines for English are not suitable for enterprise use. |
| Approach: | They propose a knowledge extraction pipeline for English which can be further used for building an entreprise-specific knowledge base. |
| Outcome: | The proposed pipeline can be used to build an entreprise-specific knowledge base. |
Knowledge Base Construction for Knowledge-Augmented Text-to-SQL (2025.findings-acl)
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Jinheon Baek, Horst Samulowitz, Oktie Hassanzadeh, Dharmashankar Subramanian, Sola Shirai, Alfio Gliozzo, Debarun Bhattacharjya
| Challenge: | Existing approaches to translate natural language queries into SQL statements are limited in their parametric knowledge of the database schemas. |
| Approach: | They propose to construct a knowledge base for text-to-SQL, a foundational source of knowledge, from which we retrieve and generate the necessary knowledge for given queries. |
| Outcome: | The proposed approach outperforms baselines on multiple text-to-SQL datasets and shows that it is practical and reliable. |
Knowledge Base Completion Meets Transfer Learning (2021.emnlp-main)
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| Challenge: | Existing knowledge bases are tedious and require a large amount of labor to build. |
| Approach: | They propose a method that allows for transfer of knowledge from one collection of facts to another without entity or relation matching. |
| Outcome: | The proposed method is the most impactful on small datasets, showing a 6% increase in rank and 65% decrease in rank over the previous best method. |
Efficient and Robust Knowledge Graph Construction (2022.aacl-tutorials)
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| Challenge: | Knowledge graph construction has appealed to the NLP community but has encountered similar issues such as efficiency and robustness. |
| Approach: | They propose to introduce efficient and robust knowledge graph construction techniques and discuss their results. |
| Outcome: | This tutorial will provide an overview of the latest and ongoing techniques for efficient and robust knowledge graph construction. |
A Study of the Importance of External Knowledge in the Named Entity Recognition Task (P18-2)
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| Challenge: | Existing studies have shown that external knowledge is important for Named Entity Recognition . |
| Approach: | They propose a modular framework that divides knowledge into four categories according to depth . they show the effects when incrementally adding deeper knowledge . |
| Outcome: | The proposed framework outperforms agnostic frameworks with more external knowledge . the proposed frameworks outperformed agrarian frameworks on two standard datasets . |
DocumentNet: Bridging the Data Gap in Document Pre-training (2023.emnlp-industry)
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| Challenge: | Document understanding tasks are a tedious task that requires extensive training and privacy constraints. |
| Approach: | They propose a method to collect weakly labeled data from the web to benefit VDER training . the collected dataset does not depend on specific document types or entity sets . |
| Outcome: | The proposed method does not depend on specific document types or entity sets, making it universally applicable to all VDER tasks. |
Summarization Beyond News: The Automatically Acquired Fandom Corpora (2020.lrec-1)
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| Challenge: | Abstractive summarization methods require large corpora to train neural architectures. |
| Approach: | They propose a novel automatic corpus construction approach that automatically constructs large open-licensed summarization corpora from existing large text collections and an evaluation process with human annotators. |
| Outcome: | The proposed approach can be used to train abstractive summarization models on large corpora and through a manual evaluation with human annotators. |
Fact Discovery from Knowledge Base via Facet Decomposition (N19-1)
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| Challenge: | Recent years have witnessed the emergence and growth of many large-scale knowledge bases (KBs) however, there are some issues unsettled towards enriching the KBs. |
| Approach: | They propose a framework that decomposes the discovery problem into several facet components and an auto-encoder component to estimate some facets of the fact. |
| Outcome: | The proposed framework achieves promising results on a benchmark dataset. |
Multi-lingual Entity Discovery and Linking (P18-5)
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| Challenge: | This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task. |
| Approach: | This tutorial will review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
| Outcome: | The aim of this tutorial is to review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |