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pro vyhledávání: '"Qian, Tieyun"'
Querying cohesive subgraphs on temporal graphs (e.g., social network, finance network, etc.) with various conditions has attracted intensive research interests recently. In this paper, we study a novel Temporal $(k,\mathcal{X})$-Core Query (TXCQ) tha
Externí odkaz:
http://arxiv.org/abs/2309.00361
Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing
Emotion recognition in conversation, which aims to predict the emotion for all utterances, has attracted considerable research attention in recent years. It is a challenging task since the recognition of the emotion in one utterance involves many com
Externí odkaz:
http://arxiv.org/abs/2306.06601
Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks. However, their ability to generate counterfactuals has not been examined systematically. To bridge this gap, we present
Externí odkaz:
http://arxiv.org/abs/2305.14791
Autor:
Li, Wanli, Qian, Tieyun
The zero-shot relation triplet extraction (ZeroRTE) task aims to extract relation triplets from a piece of text with unseen relation types. The seminal work adopts the pre-trained generative model to generate synthetic samples for new relations. Howe
Externí odkaz:
http://arxiv.org/abs/2305.01920
Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the overdetected false spans at the span detection stage and the inaccurate and unstable prototypes at the type classifica
Externí odkaz:
http://arxiv.org/abs/2302.06397
Querying cohesive subgraphs on temporal graphs with various time constraints has attracted intensive research interests recently. In this paper, we study a novel Temporal k-Core Query (TCQ) problem: given a time interval, find all distinct k-cores th
Externí odkaz:
http://arxiv.org/abs/2301.03770
The goal of relation classification (RC) is to extract the semantic relations between/among entities in the text. As a fundamental task in natural language processing, it is crucial to ensure the robustness of RC models. Despite the high accuracy cur
Externí odkaz:
http://arxiv.org/abs/2202.10668
Autor:
Li, Wanli, Qian, Tieyun
Lack of labeled data is a main obstacle in relation extraction. Semi-supervised relation extraction (SSRE) has been proven to be a promising way for this problem through annotating unlabeled samples as additional training data. Almost all prior resea
Externí odkaz:
http://arxiv.org/abs/2112.01048
Publikováno v:
In Neural Networks May 2024 173
Autor:
Zhang, Mi, Qian, Tieyun
Existing methods in relation extraction have leveraged the lexical features in the word sequence and the syntactic features in the parse tree. Though effective, the lexical features extracted from the successive word sequence may introduce some noise
Externí odkaz:
http://arxiv.org/abs/2107.13425