Challenge: Existing methods to enhance Wikipedia's reference section are not effective.
Approach: They propose a two-step approach that leverages the wikilinks present in a scientific Wikipedia target page and recommends highly relevant references to be included in that target page appropriately and automatically borrowed from the reference section of the wikipedia links.
Outcome: The proposed approach achieves a notably good performance on two datasets from Computer Science and Physics.

Similar Papers

Event Linking: Grounding Event Mentions to Wikipedia (2023.eacl-main)

Copied to clipboard

Challenge: a new task for natural language understanding is called Event Linking . the context where an event is mentioned lacks the details of this event .
Approach: They propose a new task to link an article's event mention to the most appropriate Wikipedia page . they collect a training set from Wikipedia and evaluate two models to test the task .
Outcome: The proposed model is based on a dataset and a real-world news domain . it is expected that the most appropriate Wikipedia page will provide rich knowledge about the mention .
CitationIE: Leveraging the Citation Graph for Scientific Information Extraction (2021.acl-long)

Copied to clipboard

Challenge: Existing work on scientific information extraction (SciIE) considers extraction solely based on the content of an individual paper, without considering the paper’s place in the broader literature.
Approach: They propose to automate the extraction of key information from scientific documents by leveraging a complementary source: the citation graph of referential links between citing and cited papers.
Outcome: The proposed model improves on a set of English-language scientific documents.
Boosting Entity Linking Performance by Leveraging Unlabeled Documents (P19-1)

Copied to clipboard

Challenge: a new approach to entity linking relies on unlabeled documents and Wikipedia . a supervised approach uses only natural information, such as unlabed documents .
Approach: They propose a method which exploits only naturally occurring information . they construct a high recall list of candidate entities for each mention in an unlabeled document .
Outcome: The proposed model outperforms fully-supervised state-of-the-art systems on standard test sets.
WIKIR: A Python Toolkit for Building a Large-scale Wikipedia-based English Information Retrieval Dataset (2020.lrec-1)

Copied to clipboard

Challenge: ad-hoc information retrieval methods usually require large amounts of annotated data to be effective.
Approach: They propose an open-source toolkit to automatically build large-scale English information retrieval datasets based on Wikipedia.
Outcome: The proposed toolkit builds large-scale English information retrieval datasets based on Wikipedia with 59,252 queries and 2,617,003 pairs.
CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata (2021.eacl-main)

Copied to clipboard

Challenge: Existing approaches to target end-to-end entity linking over knowledge bases are not efficient.
Approach: They propose a modular approach to target end-to-end entity linking over knowledge bases.
Outcome: The proposed approach outperforms state-of-the-art approaches on two well-known knowledge bases.
Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications (2020.lrec-1)

Copied to clipboard

Challenge: Keeping up with the most recent scientific literature is a challenge for many researchers given the continuous and increasing growth of academic publications.
Approach: They propose to use citation knowledge to provide personalised recommendations of recent scientific publications to a particular user by capturing authors’ publication history and enriched with different forms of paper citation.
Outcome: The proposed dataset captures authors’ publication history and is enriched with different forms of paper citation knowledge, namely citation graphs, citation positions, cited contexts, and citation types.
ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links (2026.eacl-long)

Copied to clipboard

Challenge: Using retrieval models and LLMs achieves a 73% approval rate for suggested links, more than doubling the acceptance of strong retrievers alone.
Approach: They propose a domain-agnostic framework for bootstrapping sentence-level cross-document links from scratch and apply it to large-scale human-in-the-loop annotation of natural text pairs.
Outcome: The proposed framework generates semi-synthetic datasets and uses them to benchmark and shortlist the best-performing methods and applies them in large-scale human-in-the-loop annotation of natural text pairs.
Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains (P19-1)

Copied to clipboard

Challenge: Existing deep learning methods require large amounts of training data to achieve reasonable performance.
Approach: They propose to generate automatic annotation suggestions for a discourse-level sequence labelling task that requires extensive domain expertise.
Outcome: The proposed model improves with newly annotated texts while introducing no biases.
On the Limitations of Reference-Free Evaluations of Generated Text (2022.emnlp-main)

Copied to clipboard

Challenge: a recent study has shown that evaluation metrics which accurately estimate the quality of generated text are limited in their ability to evaluate generated text.
Approach: They argue that reference-free metrics are limited in their ability to evaluate generated text . they recommend that they be used as diagnostic tools for analyzing and understanding model behavior .
Outcome: The proposed evaluation metrics are limited in their ability to evaluate generated text . they can be optimized at test time, can be biased against models with similar outputs .
WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types (2022.acl-long)

Copied to clipboard

Challenge: Multimodal Entity Linking (MEL) is an essential task for many multimodal applications.
Approach: They propose to use a human-annotated Wikipedia-based multimodal entity linking dataset to improve the quality of existing MEL models.
Outcome: The proposed model uses the visual information of images more effectively than existing models.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations