Search Results for author:
Found 40 papers, 14 papers with code
GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection
For Subtask B, framed as a multi-label classification problem, we utilize various improved multi-label cross-entropy loss functions and analyze the performance of our method.
Locality Preserving Sentence Encoding
Thus, cosine similarity cannot approximate distances on the manifold.
RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips
We collected and annotated a wide range of meta-data with respect to medical dialogue including doctor profiles, hospital departments, diseases and symptoms for fine-grained analysis on language usage pattern and clinical diagnosis.
基于HowNet的无监督汉语动词隐喻识别方法(Unsupervised Chinese Verb Metaphor Recognition Method Based on HowNet)
“隐喻是人类思维和语言理解的核心问题。随着互联网发展和海量文本出现, 利用自然语言处理技术对隐喻文本进行自动识别成为一种迫切的需求。但是目前在汉语隐喻识别研究中, 由于语义资源有限, 导致模型容易过拟合。此外, 主流隐喻识别方法存在可解释性差的缺点。针对上述问题, 本文构建了一个规模较大的汉语动词隐喻数据集, 并且提出了一种基于HowNet的无监督汉语动词隐喻识别模型。实验结果表明, 本文提出的模型能够有效地应用于动词隐喻识别任务, 识别效果超过了对比的无监督模型;并且, 与其它用于隐喻识别的深度学习模型相比, 本文模型具有结构简单、参数少、可解释性强的优点。”
软件标识符的自然语言规范性研究(Research on the Natural Language Normalness of Software Identifiers)
“软件源代码的理解则是软件协同开发与维护的核心, 而源代码中占半数以上的标识符的理解则在软件理解中起到重要作用, 传统软件工程主要研究通过命名规范限制标识符的命名过程以构造更易理解和交流的标识符。本文则在梳理分析常见编程语言命名规范的基础上, 提出一种全新的标识符可理解性评价标准。具体而言, 本文首先总结梳理了常见主流编程语言中的命名规范并类比自然语言语素概念本文提出基于软件语素的标识符构成过程, 即标识符的构成可被视为软件语素的生成、排列和连接过程。在此基础上, 本文提出一种结合自然语料库的软件标识符规范性评价方法, 用来衡量软件标识符是否易于理解。最后, 本文通过源代码理解数据集和乇乩乴乨乵乢平台中开源项目对规范性指标进行了验证性实验, 结果表明本文提出的规范性分数能够很好衡量软件项目的可理解性。”
基于风格化嵌入的中文文本风格迁移(Chinese text style transfer based on stylized embedding)
“对话风格能够反映对话者的属性, 例如情感、性别和教育背景等。在对话系统中, 通过理解用户的对话风格, 能够更好地对用户进行建模。同样的, 面对不同背景的用户, 对话机器人也应该使用不同的语言风格与之交流。语言表达风格是文本的内在属性, 然而现有的大多数文本风格迁移研究, 集中在英文领域, 在中文领域则研究较少。本文构建了三个可用于中文文本风格迁移研究的数据集, 并将多种已有的文本风格迁移方法应用于该数据集。同时, 本文提出了基于DeepStyle算法与Transformer的风格迁移模型, 通过预训练可以获得不同风格的隐层向量表示。并基于Transformer构建生成端模型, 在解码阶段, 通过重建源文本的方式, 保留生成文本的内容信息, 并且引入对立风格的嵌入表示, 使得模型能够生成不同风格的文本。实验结果表明, 本文提出的模型在构建的中文数据集上均优于现有模型。”
结合标签转移关系的多任务笑点识别方法(Multi-task punchlines recognition method combined with label transfer relationship)
“幽默在人类交流中扮演着重要角色, 并大量存在于情景喜剧中。笑点(punchline)是情景喜剧实现幽默效果的形式之一, 在情景喜剧笑点识别任务中, 每条句子的标签代表该句是否为笑点, 但是以往的笑点识别工作通常只通过建模上下文语义关系识别笑点, 对标签的利用并不充分。为了充分利用标签序列中的信息, 本文提出了一种新的识别方法, 即结合条件随机场的单词级-句子级多任务学习模型, 该模型在两方面进行了改进, 首先将标签序列中相邻两个标签之间的转移关系看作幽默理论中不一致性的一种体现, 并使用条件随机场学习这种转移关系, 其次由于学习相邻标签之间的转移关系以及上下文语义关系均能够学习到铺垫和笑点之间的不一致性, 两者之间存在相关性, 为了使模型通过利用这种相关性提高笑点识别的效果, 该模型引入了多任务学习方法, 使用多任务学习方法同时学习每条句子的句义、组成每条句子的所有字符的词义, 单词级别的标签转移关系以及句子级别的标签转移关系。本文在CCL2020“小牛杯”幽默计算—情景喜剧笑点识别评测任务的英文数据集上进行实验, 结果表明, 本文提出的方法比目前最好的方法提高了3. 2%, 在情景喜剧幽默笑点识别任务上取得了最好的效果, 并通过消融实验证明了上述两方面改进的有效性。”
Label-Enhanced Hierarchical Contextualized Representation for Sequential Metaphor Identification
At the sentence level, we leverage the metaphor information of words that except the target word in the sentence to strengthen the reasoning ability of our model via a novel label-enhanced contextualized representation.
Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition
Chinese Named Entity Recognition (NER) has continued to attract research attention.
基于预训练语言模型的案件要素识别方法(A Method for Case Factor Recognition Based on Pre-trained Language Models)
案件要素识别指将案件描述中重要事实描述自动抽取出来, 并根据领域专家设计的要素体系进行分类, 是智慧司法领域的重要研究内容。基于传统神经网络的文本编码难以提取深层次特征, 基于阈值的多标签分类难以捕获标签间依赖关系, 因此本文提出了基于预训练语言模型的多标签文本分类模型。该模型采用以Layer-attentive策略进行特征融合的语言模型作为编码器, 使用基于LSTM的序列生成模型作为解码器。在“CAIL2019”数据集上进行实验, 该方法比基于循环神经网络的算法在F1值上最高可提升7. 6%, 在相同超参数设置下比基础语言模型(BERT)提升约3. 2%。
基于多粒度语义交互理解网络的幽默等级识别(A Multi-Granularity Semantic Interaction Understanding Network for Humor Level Recognition)
幽默在人们日常交流中发挥着重要作用。随着人工智能的快速发展, 幽默等级识别成为自然语言处理领域的热点研究问题之一。已有的幽默等级识别研究往往将幽默文本看作一个整体, 忽视了幽默文本内部的语义关系。本文将幽默等级识别视为自然语言推理任务, 将幽默文本划分为“铺垫”和“笑点”两个部分, 分别对其语义和语义关系进行建模, 提出了一种多粒度语义交互理解网络, 从单词和子句两个粒度捕获幽默文本中语义的关联和交互。本文在Reddit公开幽默数据集上进行了实验, 相比之前最优结果, 模型在语料上的准确率提升了1. 3%。实验表明, 引入幽默内部的语义关系信息可以提高模型幽默识别的性能, 而本文提出的模型也可以很好地建模这种语义关系。
Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives
This limitation hinders a comprehensive understanding of individuals' personalities, as both stable traits and dynamic states are vital.
Disentangling ID and Modality Effects for Session-based Recommendation
At the item level, we introduce a co-occurrence representation schema to explicitly incorporate cooccurrence patterns into ID representations.
FineRec:Exploring Fine-grained Sequential Recommendation
Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors.
Side Information-Driven Session-based Recommendation: A Survey
Emerging efforts incorporate various kinds of side information into their methods for enhancing task performance.
Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks
The case study involving additional biomedical NLP tasks further shows Taiyi's considerable potential for bilingual biomedical multi-tasking.
Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation
Secondly, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling.
A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations
Emotion recognition in conversations (ERC), the task of recognizing the emotion of each utterance in a conversation, is crucial for building empathetic machines.
Ranked #1 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Recognition in Conversation
Multimodal Emotion Recognition
Beyond Co-occurrence: Multi-modal Session-based Recommendation
(2) How to fuse these heterogeneous descriptive information to comprehensively infer user interests?
KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions
Secondly, social media datasets suffer from the challenges of low annotated data.
ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generation
To overcome this challenge, we propose an innovative multimodal framework, called ZRIGF, which assimilates image-grounded information for dialogue generation in zero-resource situations.
Hate Speech Detection via Dual Contrastive Learning
Our framework jointly optimizes the self-supervised and the supervised contrastive learning loss for capturing span-level information beyond the token-level emotional semantics used in existing models, particularly detecting speech containing abusive and insulting words.
Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks
In addition, it is crucial to introduce lexical knowledge to detect the toxicity of posts, which has been a challenge for researchers.
Exploiting Pairwise Mutual Information for Knowledge-Grounded Dialogue
Correlation integration is designed to fully exploit the pairwise mutual information among dialogue context, knowledge, and responses, while overall integration adopts an integration gate to capture global information.
MultiMET: A Multimodal Dataset for Metaphor Understanding
Metaphor involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought, which makes understanding it challenging.
Hate Speech Detection Based on Sentiment Knowledge Sharing
The wanton spread of hate speech on the internet brings great harm to society and families.
Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement
We propose a Legal Triplet Extraction System for drug-related criminal judgment documents.
Biomedical named entity recognition using BERT in the machine reading comprehension framework
Using the sequence labeling framework to implement biomedical named entity recognition (BioNER) is currently a conventional method.
ALBERT-BiLSTM for Sequential Metaphor Detection
In our daily life, metaphor is a common way of expression.
Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical Knowledge
Furthermore, the Gaussian probability distribution can effectively improve the extraction performance of sentences with overlapping relations in biomedical relation extraction tasks.
Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes
To the best of our knowledge, we are the first to approach humor annotation for exploring the underlying mechanism of the use of humor, which may contribute to a significantly deeper analysis of humor.
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
Motivation: In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction.
Ranked #2 on
Named Entity Recognition (NER)
on BC4CHEMD
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition
In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns.
Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions
However, there is little research on the construction of metaphor corpora annotated with emotion for the analysis of emotionality of metaphorical expressions.
Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths
Results In this article, we present a hierarchical recurrent neural networks (RNNs)-based method to integrate the SDP and sentence sequence for DDI extraction task.
A graph kernel based on context vectors for extracting drug–drug interactions
Especially for the Medline-2013 dataset, our system outperforms the top-ranking DDIs systems by F-scores of 10. 7 and 12. 2 in detection and classification, respectively.
