Zobrazeno 1 - 10
of 412
pro vyhledávání: '"Teng, Chong"'
Structured Sentiment Analysis (SSA) was cast as a problem of bi-lexical dependency graph parsing by prior studies. Multiple formulations have been proposed to construct the graph, which share several intrinsic drawbacks: (1) The internal structures o
Externí odkaz:
http://arxiv.org/abs/2407.04801
Document-level event extraction aims to extract structured event information from unstructured text. However, a single document often contains limited event information and the roles of different event arguments may be biased due to the influence of
Externí odkaz:
http://arxiv.org/abs/2406.16021
Autor:
Gao, Qiang, Li, Bobo, Meng, Zixiang, Li, Yunlong, Zhou, Jun, Li, Fei, Teng, Chong, Ji, Donghong
Publikováno v:
LREC|COLING,Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation,2024,5907-5921
Existing cross-document event coreference resolution models, which either compute mention similarity directly or enhance mention representation by extracting event arguments (such as location, time, agent, and patient), lacking the ability to utilize
Externí odkaz:
http://arxiv.org/abs/2406.15990
Headline generation aims to summarize a long document with a short, catchy title that reflects the main idea. This requires accurately capturing the core document semantics, which is challenging due to the lengthy and background information-rich na t
Externí odkaz:
http://arxiv.org/abs/2403.15776
Multimodal Named Entity Recognition (MNER) is a pivotal task designed to extract named entities from text with the support of pertinent images. Nonetheless, a notable paucity of data for Chinese MNER has considerably impeded the progress of this natu
Externí odkaz:
http://arxiv.org/abs/2402.13693
With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions. Although prevailing methodolo
Externí odkaz:
http://arxiv.org/abs/2312.15291
Despite significant advancements in multi-label text classification, the ability of existing models to generalize to novel and seldom-encountered complex concepts, which are compositions of elementary ones, remains underexplored. This research addres
Externí odkaz:
http://arxiv.org/abs/2312.11276
It has been a hot research topic to enable machines to understand human emotions in multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion analysis in conversation (MM-ERC). MM-ERC has received consistent attention in r
Externí odkaz:
http://arxiv.org/abs/2308.04502
Autor:
Xiong, Yiyun, Dai, Mengwei, Li, Fei, Fei, Hao, Li, Bobo, Wu, Shengqiong, Ji, Donghong, Teng, Chong
Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference. This work introduces a new benchmark dataset
Externí odkaz:
http://arxiv.org/abs/2308.04498
The joint task of Dialog Sentiment Classification (DSC) and Act Recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog simultaneously. However, current methods encode the dialog context in only one directio
Externí odkaz:
http://arxiv.org/abs/2308.04424