Search Results for author: Jingbo Shang

Found 105 papers, 62 papers with code

Towards Adaptive Residual Network Training: A Neural-ODE Perspective

1 code implementation ICML 2020 chengyu dong, Liyuan Liu, Zichao Li, Jingbo Shang

Serving as a crucial factor, the depth of residual networks balances model capacity, performance, and training efficiency.

Phrase-aware Unsupervised Constituency Parsing

no code implementations ACL 2022 Xiaotao Gu, Yikang Shen, Jiaming Shen, Jingbo Shang, Jiawei Han

Recent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task.

Constituency Parsing Language Modelling +1

META: Metadata-Empowered Weak Supervision for Text Classification

1 code implementation EMNLP 2020 Dheeraj Mekala, Xinyang Zhang, Jingbo Shang

Based on seed words, we rank and filter motif instances to distill highly label-indicative ones as {``}seed motifs{''}, which provide additional weak supervision.

General Classification text-classification +2

Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty

no code implementations Findings (ACL) 2022 Zi Lin, Jeremiah Zhe Liu, Jingbo Shang

Recent work in task-independent graph semantic parsing has shifted from grammar-based symbolic approaches to neural models, showing strong performance on different types of meaning representations.

Semantic Parsing

“Average” Approximates “First Principal Component”? An Empirical Analysis on Representations from Neural Language Models

no code implementations EMNLP 2021 Zihan Wang, chengyu dong, Jingbo Shang

In this paper, we present an empirical property of these representations—”average” approximates “first principal component”.

Incubating Text Classifiers Following User Instruction with Nothing but LLM

no code implementations16 Apr 2024 Letian Peng, Jingbo Shang

In this paper, we aim to generate text classification data given arbitrary class definitions (i. e., user instruction), so one can train a small text classifier without any human annotation or raw corpus.

Prompt Engineering text-classification +1

Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving

no code implementations10 Apr 2024 Chenyang An, Zhibo Chen, Qihao Ye, Emily First, Letian Peng, Jiayun Zhang, Zihan Wang, Sorin Lerner, Jingbo Shang

Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i. e. proof steps) to search through proof states.

Automated Theorem Proving Language Modelling +1

READ: Improving Relation Extraction from an ADversarial Perspective

1 code implementation2 Apr 2024 Dawei Li, William Hogan, Jingbo Shang

This strategy enables a larger attack budget for entities and coaxes the model to leverage relational patterns embedded in the context.

Adversarial Attack Relation +1

MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks

1 code implementation30 Mar 2024 Letian Peng, Zilong Wang, Feng Yao, Zihan Wang, Jingbo Shang

We construct the distillation dataset via sampling sentences from language model pre-training datasets (e. g., OpenWebText in our implementation) and prompting an LLM to identify the typed spans of "important information".

Language Modelling named-entity-recognition +2

DOCMASTER: A Unified Platform for Annotation, Training, & Inference in Document Question-Answering

no code implementations30 Mar 2024 Alex Nguyen, Zilong Wang, Jingbo Shang, Dheeraj Mekala

The application of natural language processing models to PDF documents is pivotal for various business applications yet the challenge of training models for this purpose persists in businesses due to specific hurdles.

Privacy Preserving Question Answering

Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoning

no code implementations29 Mar 2024 Yongqi Tong, Dawei Li, Sizhe Wang, Yujia Wang, Fei Teng, Jingbo Shang

We conduct a series of experiments to prove LLMs can obtain benefits from mistakes in both directions.

TOOLVERIFIER: Generalization to New Tools via Self-Verification

1 code implementation21 Feb 2024 Dheeraj Mekala, Jason Weston, Jack Lanchantin, Roberta Raileanu, Maria Lomeli, Jingbo Shang, Jane Dwivedi-Yu

Teaching language models to use tools is an important milestone towards building general assistants, but remains an open problem.

Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models

1 code implementation16 Feb 2024 Dheeraj Mekala, Alex Nguyen, Jingbo Shang

In this paper, we introduce a novel training data selection based on the learning percentage of the samples.

MEMORYLLM: Towards Self-Updatable Large Language Models

no code implementations7 Feb 2024 Yu Wang, Xiusi Chen, Jingbo Shang, Julian McAuley

Existing Large Language Models (LLMs) usually remain static after deployment, which might make it hard to inject new knowledge into the model.

Model Editing

Learning a Decision Tree Algorithm with Transformers

1 code implementation6 Feb 2024 Yufan Zhuang, Liyuan Liu, Chandan Singh, Jingbo Shang, Jianfeng Gao

We then train MetaTree to produce the trees that achieve strong generalization performance.

Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision

no code implementations5 Feb 2024 Zihan Wang, Yunxuan Li, Yuexin Wu, Liangchen Luo, Le Hou, Hongkun Yu, Jingbo Shang

Process supervision, using a trained verifier to evaluate the intermediate steps generated by reasoner, has demonstrated significant improvements in multi-step problem solving.

GSM8K Math

Large Language Models for Time Series: A Survey

1 code implementation2 Feb 2024 Xiyuan Zhang, Ranak Roy Chowdhury, Rajesh K. Gupta, Jingbo Shang

Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision.

Quantization Time Series +1

OMNIINPUT: A Model-centric Evaluation Framework through Output Distribution

no code implementations6 Dec 2023 Weitang Liu, Ying Wai Li, Tianle Wang, Yi-Zhuang You, Jingbo Shang

We propose a novel model-centric evaluation framework, OmniInput, to evaluate the quality of an AI/ML model's predictions on all possible inputs (including human-unrecognizable ones), which is crucial for AI safety and reliability.

Less than One-shot: Named Entity Recognition via Extremely Weak Supervision

1 code implementation6 Nov 2023 Letian Peng, Zihan Wang, Jingbo Shang

We study the named entity recognition (NER) problem under the extremely weak supervision (XWS) setting, where only one example entity per type is given in a context-free way.

named-entity-recognition Named Entity Recognition +1

EmojiLM: Modeling the New Emoji Language

1 code implementation3 Nov 2023 Letian Peng, Zilong Wang, Hang Liu, Zihan Wang, Jingbo Shang

With the rapid development of the internet, online social media welcomes people with different backgrounds through its diverse content.

Language Modelling Large Language Model

ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation

no code implementations26 Oct 2023 Zi Lin, Zihan Wang, Yongqi Tong, Yangkun Wang, Yuxin Guo, Yujia Wang, Jingbo Shang

This benchmark contains the rich, nuanced phenomena that can be tricky for current toxicity detection models to identify, revealing a significant domain difference compared to social media content.

Chatbot

Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking

no code implementations18 Oct 2023 Yongqi Tong, Yifan Wang, Dawei Li, Sizhe Wang, Zi Lin, Simeng Han, Jingbo Shang

Chain-of-Thought(CoT) prompting and its variants explore equipping large language models (LLMs) with high-level reasoning abilities by emulating human-like linear cognition and logic.

Natural Language Inference

Fast-ELECTRA for Efficient Pre-training

no code implementations11 Oct 2023 chengyu dong, Liyuan Liu, Hao Cheng, Jingbo Shang, Jianfeng Gao, Xiaodong Liu

Although ELECTRA offers a significant boost in efficiency, its potential is constrained by the training cost brought by the auxiliary model.

Language Modelling

Critique Ability of Large Language Models

no code implementations7 Oct 2023 Liangchen Luo, Zi Lin, Yinxiao Liu, Lei Shu, Yun Zhu, Jingbo Shang, Lei Meng

In the era of large language models (LLMs), this study explores the ability of LLMs to deliver accurate critiques across various tasks.

Code Completion Decision Making +3

Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models

no code implementations4 Oct 2023 An Yan, Yu Wang, Yiwu Zhong, Zexue He, Petros Karypis, Zihan Wang, chengyu dong, Amilcare Gentili, Chun-Nan Hsu, Jingbo Shang, Julian McAuley

Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients.

Image Classification Language Modelling +1

Learning Concise and Descriptive Attributes for Visual Recognition

1 code implementation ICCV 2023 An Yan, Yu Wang, Yiwu Zhong, chengyu dong, Zexue He, Yujie Lu, William Wang, Jingbo Shang, Julian McAuley

Recent advances in foundation models present new opportunities for interpretable visual recognition -- one can first query Large Language Models (LLMs) to obtain a set of attributes that describe each class, then apply vision-language models to classify images via these attributes.

Descriptive

Generating Efficient Training Data via LLM-based Attribute Manipulation

1 code implementation14 Jul 2023 Letian Peng, Yuwei Zhang, Jingbo Shang

In this paper, we propose a novel method, Chain-of-Thoughts Attribute Manipulation (CoTAM), to guide few-shot learning by carefully crafted data from Large Language Models (LLMs).

Attribute Few-Shot Learning +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 Attribute Value 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.

Attribute

SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank

1 code implementation24 May 2023 Dheeraj Mekala, Adithya Samavedhi, chengyu dong, Jingbo Shang

To address the annotation bottleneck, we introduce SELFOOD, a self-supervised OOD detection method that requires only in-distribution samples as supervision.

Learning-To-Rank Out-of-Distribution Detection +1

Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image Manipulation

no code implementations24 May 2023 Prashant Krishnan, Zilong Wang, Yangkun Wang, Jingbo Shang

Recent advances of incorporating layout information, typically bounding box coordinates, into pre-trained language models have achieved significant performance in entity recognition from document images.

Image Manipulation Language Modelling +1

ClusterLLM: Large Language Models as a Guide for Text Clustering

1 code implementation24 May 2023 Yuwei Zhang, Zihan Wang, Jingbo Shang

First, we prompt ChatGPT for insights on clustering perspective by constructing hard triplet questions <does A better correspond to B than C>, where A, B and C are similar data points that belong to different clusters according to small embedder.

Clustering Language Modelling +2

Debiasing Made State-of-the-art: Revisiting the Simple Seed-based Weak Supervision for Text Classification

1 code implementation24 May 2023 chengyu dong, Zihan Wang, Jingbo Shang

We show that the limited performance of seed matching is largely due to the label bias injected by the simple seed-match rule, which prevents the classifier from learning reliable confidence for selecting high-quality pseudo-labels.

text-classification Text Classification

Text Is All You Need: Learning Language Representations for Sequential Recommendation

1 code implementation23 May 2023 Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu, Xin Shen, Jingbo Shang, Julian McAuley

In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets.

Representation Learning Sentence +1

Goal-Driven Explainable Clustering via Language Descriptions

1 code implementation23 May 2023 Zihan Wang, Jingbo Shang, Ruiqi Zhong

We propose a new task formulation, "Goal-Driven Clustering with Explanations" (GoalEx), which represents both the goal and the explanations as free-form language descriptions.

Clustering Language Modelling

A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches

1 code implementation22 May 2023 Zihan Wang, Tianle Wang, Dheeraj Mekala, Jingbo Shang

Etremely Weakly Supervised Text Classification (XWS-TC) refers to text classification based on minimal high-level human guidance, such as a few label-indicative seed words or classification instructions.

Benchmarking text-classification +1

Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting

no code implementations22 May 2023 William Hogan, Jiacheng Li, Jingbo Shang

Motivated by these insights, we present a novel method called KNoRD (Known and Novel Relation Discovery), which effectively classifies explicitly and implicitly expressed relations from known and novel classes within unlabeled data.

Relation Relation Extraction

WOT-Class: Weakly Supervised Open-world Text Classification

1 code implementation21 May 2023 Tianle Wang, Zihan Wang, Weitang Liu, Jingbo Shang

State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes of interest.

Image Classification text-classification +1

Towards Diverse and Coherent Augmentation for Time-Series Forecasting

no code implementations24 Mar 2023 Xiyuan Zhang, Ranak Roy Chowdhury, Jingbo Shang, Rajesh Gupta, Dezhi Hong

We note that augmentation designed for forecasting requires diversity as well as coherence with the original temporal dynamics.

Data Augmentation Time Series +1

Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space

no code implementations19 Feb 2023 Weitang Liu, Ying-Wai Li, Yi-Zhuang You, Jingbo Shang

We first draw the connection between the output distribution of a NN and the density of states (DOS) of a physical system.

Image Classification

Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification

1 code implementation26 Jan 2023 Zi Lin, Jeremiah Liu, Jingbo Shang

Pre-trained seq2seq models excel at graph semantic parsing with rich annotated data, but generalize worse to out-of-distribution (OOD) and long-tail examples.

Semantic Parsing Uncertainty Quantification

Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework

1 code implementation1 Jan 2023 Jiayun Zhang, Xiyuan Zhang, Xinyang Zhang, Dezhi Hong, Rajesh K. Gupta, Jingbo Shang

Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set.

Federated Learning

MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding

no code implementations27 Nov 2022 Zilong Wang, Jiuxiang Gu, Chris Tensmeyer, Nikolaos Barmpalios, Ani Nenkova, Tong Sun, Jingbo Shang, Vlad I. Morariu

In contrast, region-level models attempt to encode regions corresponding to paragraphs or text blocks into a single embedding, but they perform worse with additional word-level features.

Progressive Sentiment Analysis for Code-Switched Text Data

1 code implementation25 Oct 2022 Sudhanshu Ranjan, Dheeraj Mekala, Jingbo Shang

Instead of training on the entire code-switched corpus at once, we create buckets based on the fraction of words in the resource-rich language and progressively train from resource-rich language dominated samples to low-resource language dominated samples.

Cross-Lingual Transfer named-entity-recognition +6

WavSpA: Wavelet Space Attention for Boosting Transformers' Long Sequence Learning Ability

no code implementations5 Oct 2022 Yufan Zhuang, Zihan Wang, Fangbo Tao, Jingbo Shang

Recent works show that learning attention in the Fourier space can improve the long sequence learning capability of Transformers.

UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation

1 code implementation28 Sep 2022 Jiacheng Li, Zhankui He, Jingbo Shang, Julian McAuley

Then, to obtain personalized explanations under this framework of insertion-based generation, we design a method of incorporating aspect planning and personalized references into the insertion process.

Explainable Recommendation Explanation Generation +2

SoTeacher: A Student-oriented Teacher Network Training Framework for Knowledge Distillation

no code implementations14 Jun 2022 chengyu dong, Liyuan Liu, Jingbo Shang

To fill this gap, we propose a novel student-oriented teacher network training framework SoTeacher, inspired by recent findings that student performance hinges on teacher's capability to approximate the true label distribution of training samples.

Data Augmentation Knowledge Distillation

Leveraging QA Datasets to Improve Generative Data Augmentation

2 code implementations25 May 2022 Dheeraj Mekala, Tu Vu, Timo Schick, Jingbo Shang

The ability of generative language models (GLMs) to generate text has improved considerably in the last few years, enabling their use for generative data augmentation.

Common Sense Reasoning Data Augmentation +3

Fine-grained Contrastive Learning for Relation Extraction

1 code implementation25 May 2022 William Hogan, Jiacheng Li, Jingbo Shang

Recent relation extraction (RE) works have shown encouraging improvements by conducting contrastive learning on silver labels generated by distant supervision before fine-tuning on gold labels.

Contrastive Learning Denoising +3

LOPS: Learning Order Inspired Pseudo-Label Selection for Weakly Supervised Text Classification

1 code implementation25 May 2022 Dheeraj Mekala, chengyu dong, Jingbo Shang

Weakly supervised text classification methods typically train a deep neural classifier based on pseudo-labels.

Memorization Pseudo Label +2

Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition

1 code implementation24 May 2022 Zihan Wang, Kewen Zhao, Zilong Wang, Jingbo Shang

Fine-tuning pre-trained language models has recently become a common practice in building NLP models for various tasks, especially few-shot tasks.

Few-shot NER Language Modelling +2

WeDef: Weakly Supervised Backdoor Defense for Text Classification

no code implementations24 May 2022 Lesheng Jin, Zihan Wang, Jingbo Shang

Inspired by this observation, in WeDef, we define the reliability of samples based on whether the predictions of the weak classifier agree with their labels in the poisoned training set.

backdoor defense text-classification +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.

Attribute Language Modelling

Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework

1 code implementation Findings (ACL) 2022 Zilong Wang, Jingbo Shang

To overcome the data limitation, we propose to leverage the label surface names to better inform the model of the target entity type semantics and also embed the labels into the spatial embedding space to capture the spatial correspondence between regions and labels.

Perturbation Deterioration: The Other Side of Catastrophic Overfitting

no code implementations29 Sep 2021 Zichao Li, Liyuan Liu, chengyu dong, Jingbo Shang

While this phenomenon is commonly explained as overfitting, we observe that it is a twin process: not only does the model catastrophic overfits to one type of perturbation, but also the perturbation deteriorates into random noise.

BFClass: A Backdoor-free Text Classification Framework

no code implementations Findings (EMNLP) 2021 Zichao Li, Dheeraj Mekala, chengyu dong, Jingbo Shang

To recognize the poisoned subset, we examine the training samples with these identified triggers as the most suspicious token, and check if removing the trigger will change the poisoned model's prediction.

Backdoor Attack Language Modelling +2

Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data

no code implementations EMNLP 2021 Dheeraj Mekala, Varun Gangal, Jingbo Shang

Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples per label increases.

text-classification Text Classification +1

UCPhrase: Unsupervised Context-aware Quality Phrase Tagging

2 code implementations28 May 2021 Xiaotao Gu, Zihan Wang, Zhenyu Bi, Yu Meng, Liyuan Liu, Jiawei Han, Jingbo Shang

Training a conventional neural tagger based on silver labels usually faces the risk of overfitting phrase surface names.

Keyphrase Extraction Language Modelling +3

News Meets Microblog: Hashtag Annotation via Retriever-Generator

1 code implementation18 Apr 2021 Xiuwen Zheng, Dheeraj Mekala, Amarnath Gupta, Jingbo Shang

Hashtag annotation for microblog posts has been recently formulated as a sequence generation problem to handle emerging hashtags that are unseen in the training set.

"Average" Approximates "First Principal Component"? An Empirical Analysis on Representations from Neural Language Models

1 code implementation18 Apr 2021 Zihan Wang, chengyu dong, Jingbo Shang

In this paper, we present an empirical property of these representations -- "average" approximates "first principal component".

Unsupervised Deep Keyphrase Generation

1 code implementation18 Apr 2021 Xianjie Shen, Yinghan Wang, Rui Meng, Jingbo Shang

Keyphrase generation aims to summarize long documents with a collection of salient phrases.

Keyphrase Generation

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

Data Quality Matters For Adversarial Training: An Empirical Study

1 code implementation15 Feb 2021 chengyu dong, Liyuan Liu, Jingbo Shang

Specifically, we first propose a strategy to measure the data quality based on the learning behaviors of the data during adversarial training and find that low-quality data may not be useful and even detrimental to the adversarial robustness.

Adversarial Robustness

Sensei: Self-Supervised Sensor Name Segmentation

1 code implementation Findings (ACL) 2021 Jiaman Wu, Dezhi Hong, Rajesh Gupta, Jingbo Shang

A sensor name, typically an alphanumeric string, encodes the key context (e. g., function and location) of a sensor needed for deploying smart building applications.

Language Modelling Segmentation

SeNsER: Learning Cross-Building Sensor Metadata Tagger

1 code implementation Findings of the Association for Computational Linguistics 2020 Yang Jiao, Jiacheng Li, Jiaman Wu, Dezhi Hong, Rajesh Gupta, Jingbo Shang

Sensor metadata tagging, akin to the named entity recognition task, provides key contextual information (e. g., measurement type and location) about sensors for running smart building applications.

named-entity-recognition Named Entity Recognition +1

Overfitting or Underfitting? Understand Robustness Drop in Adversarial Training

2 code implementations15 Oct 2020 Zichao Li, Liyuan Liu, chengyu dong, Jingbo Shang

Our goal is to understand why the robustness drops after conducting adversarial training for too long.

SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery

no code implementations EMNLP 2020 Jiaming Shen, Wenda Qiu, Jingbo Shang, Michelle Vanni, Xiang Ren, Jiawei Han

To facilitate the research on studying the interplays of these two tasks, we create the first large-scale Synonym-Enhanced Set Expansion (SE2) dataset via crowdsourcing.

Contextualized Weak Supervision for Text Classification

1 code implementation ACL 2020 Dheeraj Mekala, Jingbo Shang

Weakly supervised text classification based on a few user-provided seed words has recently attracted much attention from researchers.

General Classification text-classification +1

User-Guided Aspect Classification for Domain-Specific Texts

1 code implementation30 Apr 2020 Peiran Li, Fang Guo, Jingbo Shang

Aspect classification, identifying aspects of text segments, facilitates numerous applications, such as sentiment analysis and review summarization.

General Classification Sentiment Analysis +2

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

SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble

1 code implementation17 Oct 2019 Jiaming Shen, Zeqiu Wu, Dongming Lei, Jingbo Shang, Xiang Ren, Jiawei Han

In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features.

feature selection Question Answering

FUSE: Multi-Faceted Set Expansion by Coherent Clustering of Skip-grams

1 code implementation10 Oct 2019 Wanzheng Zhu, Hongyu Gong, Jiaming Shen, Chao Zhang, Jingbo Shang, Suma Bhat, Jiawei Han

In this paper, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and return multiple sets of entities, one for each semantic facet.

Clustering Language Modelling

Raw-to-End Name Entity Recognition in Social Media

1 code implementation14 Aug 2019 Liyuan Liu, Zihan Wang, Jingbo Shang, Dandong Yin, Heng Ji, Xiang Ren, Shaowen Wang, Jiawei Han

Our model neither requires the conversion from character sequences to word sequences, nor assumes tokenizer can correctly detect all word boundaries.

named-entity-recognition Named Entity Recognition +1

Arabic Named Entity Recognition: What Works and What's Next

no code implementations WS 2019 Liyuan Liu, Jingbo Shang, Jiawei Han

This paper presents the winning solution to the Arabic Named Entity Recognition challenge run by Topcoder. com.

Ensemble Learning Feature Engineering +4

Learning Named Entity Tagger using Domain-Specific Dictionary

1 code implementation EMNLP 2018 Jingbo Shang, Liyuan Liu, Xiang Ren, Xiaotao Gu, Teng Ren, Jiawei Han

Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features.

named-entity-recognition Named Entity Recognition +1

Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach

1 code implementation29 Apr 2018 Jiaming Shen, Jinfeng Xiao, Xinwei He, Jingbo Shang, Saurabh Sinha, Jiawei Han

Different from Web or general domain search, a large portion of queries in scientific literature search are entity-set queries, that is, multiple entities of possibly different types.

Model Selection

Integrating Local Context and Global Cohesiveness for Open Information Extraction

1 code implementation26 Apr 2018 Qi Zhu, Xiang Ren, Jingbo Shang, Yu Zhang, Ahmed El-Kishky, Jiawei Han

However, current Open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions.

Open Information Extraction Relation +1

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling

1 code implementation EMNLP 2018 Liyuan Liu, Xiang Ren, Jingbo Shang, Jian Peng, Jiawei Han

Many efforts have been made to facilitate natural language processing tasks with pre-trained language models (LMs), and brought significant improvements to various applications.

Language Modelling Named Entity Recognition (NER)

Investigating Rumor News Using Agreement-Aware Search

1 code implementation21 Feb 2018 Jingbo Shang, Tianhang Sun, Jiaming Shen, Xingbang Liu, Anja Gruenheid, Flip Korn, Adam Lelkes, Cong Yu, Jiawei Han

We build Maester based on the following two key observations: (1) relatedness can commonly be determined by keywords and entities occurring in both questions and articles, and (2) the level of agreement between the investigative question and the related news article can often be decided by a few key sentences.

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

2 code implementations30 Jan 2018 Xuan Wang, Yu Zhang, Xiang Ren, Yuhao Zhang, Marinka Zitnik, Jingbo Shang, Curtis Langlotz, Jiawei Han

Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases.

Feature Engineering Multi-Task Learning +4

An Attention-based Collaboration Framework for Multi-View Network Representation Learning

1 code implementation19 Sep 2017 Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han

Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network.

Representation Learning

Empower Sequence Labeling with Task-Aware Neural Language Model

3 code implementations13 Sep 2017 Liyuan Liu, Jingbo Shang, Frank F. Xu, Xiang Ren, Huan Gui, Jian Peng, Jiawei Han

In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task.

Language Modelling named-entity-recognition +5

MetaPAD: Meta Pattern Discovery from Massive Text Corpora

no code implementations13 Mar 2017 Meng Jiang, Jingbo Shang, Taylor Cassidy, Xiang Ren, Lance M. Kaplan, Timothy P. Hanratty, Jiawei Han

We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise.

Dependency Parsing

Automated Phrase Mining from Massive Text Corpora

4 code implementations15 Feb 2017 Jingbo Shang, Jialu Liu, Meng Jiang, Xiang Ren, Clare R. Voss, Jiawei Han

As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus.

General Knowledge POS +1

DPPred: An Effective Prediction Framework with Concise Discriminative Patterns

no code implementations31 Oct 2016 Jingbo Shang, Meng Jiang, Wenzhu Tong, Jinfeng Xiao, Jian Peng, Jiawei Han

In the literature, two series of models have been proposed to address prediction problems including classification and regression.

Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks

1 code implementation31 Oct 2016 Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, Jian Peng

It models vertices as low-dimensional vectors to explore network structure-embedded similarity.

A Parallel and Efficient Algorithm for Learning to Match

no code implementations22 Oct 2014 Jingbo Shang, Tianqi Chen, Hang Li, Zhengdong Lu, Yong Yu

In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization.

Collaborative Filtering Link Prediction

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