Search Results for author: Chenwei Zhang

Found 48 papers, 17 papers with code

Visualizing DNA reaction trajectories with deep graph embedding approaches

1 code implementation6 Nov 2023 Chenwei Zhang, Khanh Dao Duc, Anne Condon

Synthetic biologists and molecular programmers design novel nucleic acid reactions, with many potential applications.

Dimensionality Reduction Graph Embedding

ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddings

1 code implementation6 Nov 2023 Chenwei Zhang, Jordan Lovrod, Boyan Beronov, Khanh Dao Duc, Anne Condon

Visualization tools can help synthetic biologists and molecular programmers understand the complex reactive pathways of nucleic acid reactions, which can be designed for many potential applications and can be modelled using a continuous-time Markov chain (CTMC).

Dimensionality Reduction

EMPOT: partial alignment of density maps and rigid body fitting using unbalanced Gromov-Wasserstein divergence

no code implementations1 Nov 2023 Aryan Tajmir Riahi, Chenwei Zhang, James Chen, Anne Condon, Khanh Dao Duc

Aligning EM density maps and fitting atomic models are essential steps in single particle cryogenic electron microscopy (cryo-EM), with recent methods leveraging various algorithms and machine learning tools.

Benchmarking Cryogenic Electron Microscopy (cryo-EM)

CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks

no code implementations23 Oct 2023 Hoang H. Nguyen, Ye Liu, Chenwei Zhang, Tao Zhang, Philip S. Yu

While Chain-of-Thought prompting is popular in reasoning tasks, its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored.

Natural Language Understanding

Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning

1 code implementation9 Aug 2023 Hoang H. Nguyen, Chenwei Zhang, Ye Liu, Philip S. Yu

Recent advanced methods in Natural Language Understanding for Task-oriented Dialogue (TOD) Systems (e. g., intent detection and slot filling) require a large amount of annotated data to achieve competitive performance.

Contrastive Learning Intent Detection +4

Enhancing Cross-lingual Transfer via Phonemic Transcription Integration

1 code implementation10 Jul 2023 Hoang H. Nguyen, Chenwei Zhang, Tao Zhang, Eugene Rohrbaugh, Philip S. Yu

Particularly, we propose unsupervised alignment objectives to capture (1) local one-to-one alignment between the two different modalities, (2) alignment via multi-modality contexts to leverage information from additional modalities, and (3) alignment via multilingual contexts where additional bilingual dictionaries are incorporated.

Cross-Lingual Transfer named-entity-recognition +3

PV2TEA: Patching Visual Modality to Textual-Established Information Extraction

no code implementations1 Jun 2023 Hejie Cui, Rongmei Lin, Nasser Zalmout, Chenwei Zhang, Jingbo Shang, Carl Yang, Xian Li

Information extraction, e. g., attribute value extraction, has been extensively studied and formulated based only on text.

Attribute Value Extraction

GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks

no code implementations26 May 2023 Xuming Hu, Aiwei Liu, Zeqi Tan, Xin Zhang, Chenwei Zhang, Irwin King, Philip S. Yu

These techniques neither preserve the semantic consistency of the original sentences when rule-based augmentations are adopted, nor preserve the syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations.

Data Augmentation Relation Extraction

Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach

1 code implementation26 May 2023 Liyan Xu, Chenwei Zhang, Xian Li, Jingbo Shang, Jinho D. Choi

We present a new task setting for attribute mining on e-commerce products, serving as a practical solution to extract open-world attributes without extensive human intervention.

Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction

1 code implementation2 May 2023 Xuming Hu, Zhaochen Hong, Chenwei Zhang, Irwin King, Philip S. Yu

Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role.

Relation Extraction

HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction

1 code implementation NAACL 2022 Xuming Hu, Shuliang Liu, Chenwei Zhang, Shu`ang Li, Lijie Wen, Philip S. Yu

Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution.

Clustering Contrastive Learning +1

OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision

1 code implementation29 Apr 2022 Xinyang Zhang, Chenwei Zhang, Xian Li, Xin Luna Dong, Jingbo Shang, Christos Faloutsos, Jiawei Han

Most prior works on this matter mine new values for a set of known attributes but cannot handle new attributes that arose from constantly changing data.

Language Modelling

Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction

1 code implementation EMNLP 2021 Xuming Hu, Chenwei Zhang, Yawen Yang, Xiaohe Li, Li Lin, Lijie Wen, Philip S. Yu

Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.

Meta-Learning Pseudo Label +4

Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks

no code implementations23 Feb 2021 Xinyang Zhang, Chenwei Zhang, Luna Xin Dong, Jingbo Shang, Jiawei Han

Specifically, we jointly train two modules with different inductive biases -- a text analysis module for text understanding and a network learning module for class-discriminative, scalable network learning.

Product Categorization Text Categorization

Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection

1 code implementation Findings of the Association for Computational Linguistics 2020 Hoang Nguyen, Chenwei Zhang, Congying Xia, Philip S. Yu

Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components.

Few-Shot Learning Generalized Few-Shot Learning +1

Semi-supervised Relation Extraction via Incremental Meta Self-Training

1 code implementation Findings (EMNLP) 2021 Xuming Hu, Chenwei Zhang, Fukun Ma, Chenyao Liu, Lijie Wen, Philip S. Yu

To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples.

Meta-Learning Pseudo Label +1

Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering

no code implementations6 Aug 2020 Ye Liu, Shaika Chowdhury, Chenwei Zhang, Cornelia Caragea, Philip S. Yu

Unlike most other QA tasks that focus on linguistic understanding, HeadQA requires deeper reasoning involving not only knowledge extraction, but also complex reasoning with healthcare knowledge.

Multiple-choice Question Answering

Octet: Online Catalog Taxonomy Enrichment with Self-Supervision

no code implementations18 Jun 2020 Yuning Mao, Tong Zhao, Andrey Kan, Chenwei Zhang, Xin Luna Dong, Christos Faloutsos, Jiawei Han

We propose to distantly train a sequence labeling model for term extraction and employ graph neural networks (GNNs) to capture the taxonomy structure as well as the query-item-taxonomy interactions for term attachment.

Term Extraction

CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection

no code implementations4 Apr 2020 Congying Xia, Chenwei Zhang, Hoang Nguyen, Jiawei Zhang, Philip Yu

In this paper, we formulate a more realistic and difficult problem setup for the intent detection task in natural language understanding, namely Generalized Few-Shot Intent Detection (GFSID).

Conditional Text Generation Intent Detection +3

Med2Meta: Learning Representations of Medical Concepts with Meta-Embeddings

no code implementations6 Dec 2019 Shaika Chowdhury, Chenwei Zhang, Philip S. Yu, Yuan Luo

Distributed representations of medical concepts have been used to support downstream clinical tasks recently.

Decision Making

Generative Temporal Link Prediction via Self-tokenized Sequence Modeling

no code implementations26 Nov 2019 Yue Wang, Chenwei Zhang, Shen Wang, Philip S. Yu, Lu Bai, Lixin Cui, Guandong Xu

We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks.

Link Prediction

Hierarchical Semantic Correspondence Learning for Post-Discharge Patient Mortality Prediction

no code implementations15 Oct 2019 Shaika Chowdhury, Chenwei Zhang, Philip S. Yu, Yuan Luo

Predicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients.

Mortality Prediction Semantic correspondence

Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking

no code implementations13 Aug 2019 Yue Wang, Yao Wan, Chenwei Zhang, Lixin Cui, Lu Bai, Philip S. Yu

During the iterations, our model updates the parallel policies and the corresponding scenario-based regrets for agents simultaneously.

Decision Making Multi-agent Reinforcement Learning +2

Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System

1 code implementation13 Aug 2019 Ye Liu, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip S. Yu

To improve the quality and retrieval performance of the generated questions, we make two major improvements: 1) To better encode the semantics of ill-formed questions, we enrich the representation of questions with character embedding and the recent proposed contextual word embedding such as BERT, besides the traditional context-free word embeddings; 2) To make it capable to generate desired questions, we train the model with deep reinforcement learning techniques that considers an appropriate wording of the generation as an immediate reward and the correlation between generated question and answer as time-delayed long-term rewards.

Question Answering reinforcement-learning +3

Structured Knowledge Discovery from Massive Text Corpus

no code implementations23 Jul 2019 Chenwei Zhang

In particular, four problems are studied in this dissertation: Structured Intent Detection for Natural Language Understanding, Structure-aware Natural Language Modeling, Generative Structured Knowledge Expansion, and Synonym Refinement on Structured Knowledge.

Intent Detection Language Modelling +1

Multi-Grained Named Entity Recognition

1 code implementation ACL 2019 Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.

Multi-Grained Named Entity Recognition named-entity-recognition +4

Missing Movie Synergistic Completion across Multiple Isomeric Online Movie Knowledge Libraries

no code implementations15 May 2019 Bowen Dong, Jiawei Zhang, Chenwei Zhang, Yang Yang, Philip S. Yu

Online knowledge libraries refer to the online data warehouses that systematically organize and categorize the knowledge-based information about different kinds of concepts and entities.

Entity Synonym Discovery via Multipiece Bilateral Context Matching

1 code implementation31 Dec 2018 Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization.

Entity Disambiguation

Joint Slot Filling and Intent Detection via Capsule Neural Networks

3 code implementations ACL 2019 Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding.

Intent Detection Natural Language Understanding +1

Data-driven Blockbuster Planning on Online Movie Knowledge Library

no code implementations24 Oct 2018 Ye Liu, Jiawei Zhang, Chenwei Zhang, Philip S. Yu

After a thorough investigation of an online movie knowledge library, a novel movie planning framework "Blockbuster Planning with Maximized Movie Configuration Acquaintance" (BigMovie) is introduced in this paper.

Finding Similar Medical Questions from Question Answering Websites

no code implementations14 Oct 2018 Yaliang Li, Liuyi Yao, Nan Du, Jing Gao, Qi Li, Chuishi Meng, Chenwei Zhang, Wei Fan

Patients who have medical information demands tend to post questions about their health conditions on these crowdsourced Q&A websites and get answers from other users.

Question Answering Retrieval

SynonymNet: Multi-context Bilateral Matching for Entity Synonyms

no code implementations27 Sep 2018 Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Being able to automatically discover synonymous entities from a large free-text corpus has transformative effects on structured knowledge discovery.

DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

no code implementations23 Mar 2018 Bokai Cao, Lei Zheng, Chenwei Zhang, Philip S. Yu, Andrea Piscitello, John Zulueta, Olu Ajilore, Kelly Ryan, Alex D. Leow

The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives.

Multi-Task Pharmacovigilance Mining from Social Media Posts

no code implementations19 Jan 2018 Shaika Chowdhury, Chenwei Zhang, Philip S. Yu

Social media has grown to be a crucial information source for pharmacovigilance studies where an increasing number of people post adverse reactions to medical drugs that are previously unreported.

Generative Discovery of Relational Medical Entity Pairs

no code implementations ICLR 2018 Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu

Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies.

BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder

no code implementations26 Nov 2017 Jiawei Zhang, Congying Xia, Chenwei Zhang, Limeng Cui, Yanjie Fu, Philip S. Yu

The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network.

Social and Information Networks Databases

Bringing Semantic Structures to User Intent Detection in Online Medical Queries

no code implementations22 Oct 2017 Chenwei Zhang, Nan Du, Wei Fan, Yaliang Li, Chun-Ta Lu, Philip S. Yu

The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries.

Intent Detection Multi-Task Learning

Multi-source Hierarchical Prediction Consolidation

no code implementations11 Aug 2016 Chenwei Zhang, Sihong Xie, Yaliang Li, Jing Gao, Wei Fan, Philip S. Yu

We propose a novel multi-source hierarchical prediction consolidation method to effectively exploits the complicated hierarchical label structures to resolve the noisy and conflicting information that inherently originates from multiple imperfect sources.

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