Search Results for author: Bowen Yu

Found 27 papers, 13 papers with code

Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph

1 code implementation ACL 2022 Yanzeng Li, Jiangxia Cao, Xin Cong, Zhenyu Zhang, Bowen Yu, Hongsong Zhu, Tingwen Liu

Chinese pre-trained language models usually exploit contextual character information to learn representations, while ignoring the linguistics knowledge, e. g., word and sentence information.

Language Modelling

Maximal Clique Based Non-Autoregressive Open Information Extraction

no code implementations EMNLP 2021 Bowen Yu, Yucheng Wang, Tingwen Liu, Hongsong Zhu, Limin Sun, Bin Wang

However, the popular OpenIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded ones, which enforce an unnecessary order on the facts and involve the error accumulation between autoregressive steps.

Open Information Extraction

Profiling and Evolution of Intellectual Property

no code implementations20 Apr 2022 Bowen Yu, Yingxia Shao, Ang Li

In recent years, with the rapid growth of Internet data, the number and types of scientific and technological resources are also rapidly expanding.

Web Page Content Extraction Based on Multi-feature Fusion

no code implementations21 Mar 2022 Bowen Yu, Junping Du, Yingxia Shao

With the rapid growth of the number and types of web resources, there are still problems to be solved when using a single strategy to extract the text information of different pages.

Document-Level Event Extraction via Human-Like Reading Process

no code implementations7 Feb 2022 Shiyao Cui, Xin Cong, Bowen Yu, Tingwen Liu, Yucheng Wang, Jinqiao Shi

Meanwhile, rough reading is explored in a multi-round manner to discover undetected events, thus the multi-events problem is handled.

Document-level Event Extraction Event Extraction

Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning

1 code implementation EMNLP 2021 Xinghua Zhang, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Jiawei Sheng, Mengge Xue, Hongbo Xu

Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision.

Denoising Named Entity Recognition +1

Label Enhanced Event Detection with Heterogeneous Graph Attention Networks

no code implementations3 Dec 2020 Shiyao Cui, Bowen Yu, Xin Cong, Tingwen Liu, Quangang Li, Jinqiao Shi

A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction.

Event Detection Graph Attention

Porous Lattice Transformer Encoder for Chinese NER

no code implementations COLING 2020 Xue Mengge, Bowen Yu, Tingwen Liu, Yue Zhang, Erli Meng, Bin Wang

Incorporating lexicons into character-level Chinese NER by lattices is proven effective to exploitrich word boundary information.

NER

Document-level Relation Extraction with Dual-tier Heterogeneous Graph

no code implementations COLING 2020 Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Tingwen Liu, Hengzhu Tang, Wang Yubin, Li Guo

Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result.

Decision Making Relation Extraction

TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking

1 code implementation COLING 2020 Yucheng Wang, Bowen Yu, Yueyang Zhang, Tingwen Liu, Hongsong Zhu, Limin Sun

To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while immune from the exposure bias.

Relation Extraction

Coarse-to-Fine Pre-training for Named Entity Recognition

1 code implementation EMNLP 2020 Mengge Xue, Bowen Yu, Zhenyu Zhang, Tingwen Liu, Yue Zhang, Bin Wang

More recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT.

Named Entity Recognition NER

Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering

1 code implementation23 Jun 2020 Xin Cong, Bowen Yu, Tingwen Liu, Shiyao Cui, Hengzhu Tang, Bin Wang

We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner.

Classification General Classification +1

Enhancing Pre-trained Chinese Character Representation with Word-aligned Attention

1 code implementation ACL 2020 Yanzeng Li, Bowen Yu, Mengge Xue, Tingwen Liu

Most Chinese pre-trained models take character as the basic unit and learn representation according to character's external contexts, ignoring the semantics expressed in the word, which is the smallest meaningful utterance in Chinese.

Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy

1 code implementation10 Sep 2019 Bowen Yu, Zhen-Yu Zhang, Xiaobo Shu, Yubin Wang, Tingwen Liu, Bin Wang, Sujian Li

Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model.

Relation Extraction

FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System

1 code implementation2 Aug 2019 Bowen Yu, Claudio T. Silva

Dataflow visualization systems enable flexible visual data exploration by allowing the user to construct a dataflow diagram that composes query and visualization modules to specify system functionality.

Machine Translation

Visus: An Interactive System for Automatic Machine Learning Model Building and Curation

no code implementations5 Jul 2019 Aécio Santos, Sonia Castelo, Cristian Felix, Jorge Piazentin Ono, Bowen Yu, Sungsoo Hong, Cláudio T. Silva, Enrico Bertini, Juliana Freire

In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems.

AutoML

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