Výsledky vyhledávání - "You, Huiling"

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JSEEGraph: Joint Structured Event Extraction as Graph Parsing

Autor: You, Huiling, Touileb, Samia, Øvrelid, Lilja

We propose a graph-based event extraction framework JSEEGraph that approaches the task of event extraction as general graph parsing in the tradition of Meaning Representation Parsing. It explicitly encodes entities and events in a single semantic gra

Externí odkaz: http://arxiv.org/abs/2306.14633

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EventGraph at CASE 2021 Task 1: A General Graph-based Approach to Protest Event Extraction

Autor: You, Huiling, Samuel, David, Touileb, Samia, Øvrelid, Lilja

This paper presents our submission to the 2022 edition of the CASE 2021 shared task 1, subtask 4. The EventGraph system adapts an end-to-end, graph-based semantic parser to the task of Protest Event Extraction and more specifically subtask 4 on event

Externí odkaz: http://arxiv.org/abs/2210.09770

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EventGraph: Event Extraction as Semantic Graph Parsing

Autor: You, Huiling, Samuel, David, Touileb, Samia, Øvrelid, Lilja

Event extraction involves the detection and extraction of both the event triggers and corresponding event arguments. Existing systems often decompose event extraction into multiple subtasks, without considering their possible interactions. In this pa

Externí odkaz: http://arxiv.org/abs/2210.08646

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Dissertation/ Thesis

Unsupervised Lexical Semantic Change Detection with Context-Dependent Word Representations

Autor: You, Huiling

In this work, we explore the usefulness of contextualized embeddings from language models on lexical semantic change (LSC) detection. With diachronic corpora spanning two time periods, we construct word embeddings for a selected set of target words,

Externí odkaz: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444871

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Uppsala NLP at SemEval-2021 Task 2: Multilingual Language Models for Fine-tuning and Feature Extraction in Word-in-Context Disambiguation

Autor: You, Huiling, Zhu, Xingran, Stymne, Sara

We describe the Uppsala NLP submission to SemEval-2021 Task 2 on multilingual and cross-lingual word-in-context disambiguation. We explore the usefulness of three pre-trained multilingual language models, XLM-RoBERTa (XLMR), Multilingual BERT (mBERT)

Externí odkaz: http://arxiv.org/abs/2104.03767

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