Search Results for author: Yunyi Zhang

Found 20 papers, 16 papers with code

TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision

no code implementations29 Feb 2024 Yunyi Zhang, Ruozhen Yang, Xueqiang Xu, Jinfeng Xiao, Jiaming Shen, Jiawei Han

On the other hand, previous weakly-supervised hierarchical text classification methods only utilize the raw taxonomy skeleton and ignore the rich information hidden in the text corpus that can serve as additional class-indicative features.

text-classification Text Classification

A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion

1 code implementation20 Feb 2024 Yanzhen Shen, Yu Zhang, Yunyi Zhang, Jiawei Han

Entity Set Expansion, Taxonomy Expansion, and Seed-Guided Taxonomy Construction are three representative tasks that can be used to automatically populate an existing taxonomy with new entities.

Language Modelling Large Language Model +1

Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains

1 code implementation23 Jan 2024 Yu Zhang, Yunyi Zhang, Yanzhen Shen, Yu Deng, Lucian Popa, Larisa Shwartz, ChengXiang Zhai, Jiawei Han

In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to classify new entity mentions into both seen and unseen types (i. e., those without seed entities).

Entity Typing Natural Language Inference

Ontology Enrichment for Effective Fine-grained Entity Typing

no code implementations11 Oct 2023 Siru Ouyang, Jiaxin Huang, Pranav Pillai, Yunyi Zhang, Yu Zhang, Jiawei Han

In this study, we propose OnEFET, where we (1) enrich each node in the ontology structure with two types of extra information: instance information for training sample augmentation and topic information to relate types to contexts, and (2) develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples.

Entity Typing

Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers

1 code implementation24 Jun 2023 Yu Zhang, Bowen Jin, Xiusi Chen, Yanzhen Shen, Yunyi Zhang, Yu Meng, Jiawei Han

Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descriptions (e. g., category names, category-indicative keywords).

Multi-Label Classification

PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training

1 code implementation23 May 2023 Yunyi Zhang, Minhao Jiang, Yu Meng, Yu Zhang, Jiawei Han

Weakly-supervised text classification trains a classifier using the label name of each target class as the only supervision, which largely reduces human annotation efforts.

Pseudo Label Sentiment Analysis +3

Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding

1 code implementation8 Apr 2023 Susik Yoon, Dongha Lee, Yunyi Zhang, Jiawei Han

Unsupervised discovery of stories with correlated news articles in real-time helps people digest massive news streams without expensive human annotations.

Sentence

Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts

1 code implementation12 Dec 2022 Yu Zhang, Yunyi Zhang, Martin Michalski, Yucheng Jiang, Yu Meng, Jiawei Han

Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user's interest.

Language Modelling Word Embeddings

Entity Set Co-Expansion in StackOverflow

no code implementations5 Dec 2022 Yu Zhang, Yunyi Zhang, Yucheng Jiang, Martin Michalski, Yu Deng, Lucian Popa, ChengXiang Zhai, Jiawei Han

Given a few seed entities of a certain type (e. g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds.

graph construction Management

Unsupervised Key Event Detection from Massive Text Corpora

1 code implementation8 Jun 2022 Yunyi Zhang, Fang Guo, Jiaming Shen, Jiawei Han

Automated event detection from news corpora is a crucial task towards mining fast-evolving structured knowledge.

Event Detection

Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations

1 code implementation9 Feb 2022 Yu Meng, Yunyi Zhang, Jiaxin Huang, Yu Zhang, Jiawei Han

Interestingly, there have not been standard approaches to deploy PLMs for topic discovery as better alternatives to topic models.

Clustering Language Modelling +1

Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

1 code implementation EMNLP 2021 Yu Meng, Yunyi Zhang, Jiaxin Huang, Xuan Wang, Yu Zhang, Heng Ji, Jiawei Han

We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base.

Language Modelling named-entity-recognition +2

Corpus-based Open-Domain Event Type Induction

1 code implementation EMNLP 2021 Jiaming Shen, Yunyi Zhang, Heng Ji, Jiawei Han

As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of <predicate sense, object head> pairs.

Event Extraction Object +1

Text Classification Using Label Names Only: A Language Model Self-Training Approach

2 code implementations EMNLP 2020 Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, Jiawei Han

In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents.

Document Classification General Classification +6

CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring

1 code implementation13 Oct 2020 Jiaxin Huang, Yiqing Xie, Yu Meng, Yunyi Zhang, Jiawei Han

Taxonomy is not only a fundamental form of knowledge representation, but also crucial to vast knowledge-rich applications, such as question answering and web search.

Question Answering Relation

Ridge Regression Revisited: Debiasing, Thresholding and Bootstrap

no code implementations17 Sep 2020 Yunyi Zhang, Dimitris N. Politis

Extensive numerical simulations further show that the debiased and thresholded ridge regression has favorable finite sample performance and may be preferable in some settings.

Model Selection Prediction Intervals +1

Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding

1 code implementation18 Jul 2020 Yu Meng, Yunyi Zhang, Jiaxin Huang, Yu Zhang, Chao Zhang, Jiawei Han

Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora.

text-classification Topic Models

Empower Entity Set Expansion via Language Model Probing

1 code implementation ACL 2020 Yunyi Zhang, Jiaming Shen, Jingbo Shang, Jiawei Han

Existing set expansion methods bootstrap the seed entity set by adaptively selecting context features and extracting new entities.

Language Modelling Question Answering

Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion

1 code implementation27 Jan 2020 Jiaxin Huang, Yiqing Xie, Yu Meng, Jiaming Shen, Yunyi Zhang, Jiawei Han

Given a small set of seed entities (e. g., ``USA'', ``Russia''), corpus-based set expansion is to induce an extensive set of entities which share the same semantic class (Country in this example) from a given corpus.

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