Information Extraction (IE) spans several tasks with different output structures, such as named entity recognition, relation extraction and event extraction. Previously, those tasks were solved with different models because of diverse task output structures. Through re-examining IE tasks, we find that all of them can be interpreted as extracting spans and span relations. They can further be decomposed into token-pair classification tasks by using the start and end token of a span to pinpoint the span, and using the start-to-start and end-to-end token pairs of two spans to determine the relation. Based on the reformulation, we propose a Unified Token-pair Classification architecture for Information Extraction (UTC-IE), where we introduce Plusformer on top of the token-pair feature matrix. Specifically, it models axis-aware interaction with plus-shaped self-attention and local interaction with Convolutional Neural Network over token pairs. Experiments show that our approach outperforms task-specific and unified models on all tasks in 10 datasets, and achieves better or comparable results on 2 joint IE datasets. Moreover, UTC-IE speeds up over state-of-the-art models on IE tasks significantly in most datasets, which verifies the effectiveness of our architecture.

Relationship extraction

Feature (linguistics)

10.18653/v1/2023.acl-long.226

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Citations (4)

An Intelligent and Efficient Rehabilitation Status Evaluation Method: A Case Study on Stroke Patients

Lecture notes in computer science (2021)

Yao Tong Hang Yan Xin Li Gang Chen Zhenxiang Zhang

10.1007/978-3-030-71055-2_10

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Citations (1)

A Unified Generative Framework for Various NER Subtasks

Hang Yan Tao Gui Junqi Dai Qipeng Guo Zheng Zhang

Hang Yan, Tao Gui, Junqi Dai, Qipeng Guo, Zheng Zhang, Xipeng Qiu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Zhàng

Computational linguistics

10.18653/v1/2021.acl-long.451

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Citations (200)

Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa

arXiv (Cornell University) (2021)

Junqi Dai Hang Yan Tianxiang Sun Pengfei Liu Xipeng Qiu

Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarities for aspects, is a fine-grained task in the field of sentiment analysis. Previous work showed syntactic information, e.g. dependency trees, can effectively improve the ABSA performance. Recently, pre-trained models (PTMs) also have shown their effectiveness on ABSA. Therefore, the question naturally arises whether PTMs contain sufficient syntactic information for ABSA so that we can obtain a good ABSA model only based on PTMs. In this paper, we firstly compare the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa (FT-RoBERTa) outperforms the parser-provided tree. The further analysis experiments reveal that the FT-RoBERTa Induced Tree is more sentiment-word-oriented and could benefit the ABSA task. The experiments also show that the pure RoBERTa-based model can outperform or approximate to the previous SOTA performances on six datasets across four languages since it implicitly incorporates the task-oriented syntactic information.

Dependency grammar

Sentiment Analysis

Tree (set theory)

Baseline (sea)

10.48550/arxiv.2104.04986

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Citations (4)

RIDI: Robust IMU Double Integration

arXiv (Cornell University) (2017)

Hang Yan Qi Shan Yasutaka Furukawa

This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone. The key observation is that human motions are repetitive and consist of a few major modes (e.g., standing, walking, or turning). Our algorithm regresses a velocity vector from the history of linear accelerations and angular velocities, then corrects low-frequency bias in the linear accelerations, which are integrated twice to estimate positions. We have acquired training data with ground-truth motions across multiple human subjects and multiple phone placements (e.g., in a bag or a hand). The qualitatively and quantitatively evaluations have demonstrated that our algorithm has surprisingly shown comparable results to full Visual Inertial navigation. To our knowledge, this paper is the first to integrate sophisticated machine learning techniques with inertial navigation, potentially opening up a new line of research in the domain of data-driven inertial navigation. We will publicly share our code and data to facilitate further research.

Units of measurement

Ground truth

Code (set theory)

10.48550/arxiv.1712.09004

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Citations (1)

BART-Reader: Predicting Relations Between Entities via Reading Their Document-Level Context Information

Lecture notes in computer science (2022)

Hang Yan Yu Sun Junqi Dai Xiangkun Hu Qipeng Guo

Sequence (biology)

Representation

Relationship extraction