Search Results for author: Yiqun Liu

Found 73 papers, 40 papers with code

Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval

no code implementations1 Apr 2024 Haitao Li, You Chen, Zhekai Ge, Qingyao Ai, Yiqun Liu, Quan Zhou, Shuai Huo

Legal retrieval techniques play an important role in preserving the fairness and equality of the judicial system.

Fairness Learning-To-Rank +2

EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation

no code implementations1 Apr 2024 Shaorun Zhang, Zhiyu He, Ziyi Ye, Peijie Sun, Qingyao Ai, Min Zhang, Yiqun Liu

To address these challenges and provide a more comprehensive understanding of user affective experience and cognitive activity, we propose EEG-SVRec, the first EEG dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation.

EEG Recommendation Systems

Common Sense Enhanced Knowledge-based Recommendation with Large Language Model

1 code implementation27 Mar 2024 Shenghao Yang, Weizhi Ma, Peijie Sun, Min Zhang, Qingyao Ai, Yiqun Liu, Mingchen Cai

Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.

Common Sense Reasoning Knowledge Graphs +3

Sequential Recommendation with Latent Relations based on Large Language Model

1 code implementation27 Mar 2024 Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai, Yiqun Liu, Mingchen Cai, Min Zhang

Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items.

Collaborative Filtering Knowledge Graphs +5

DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment

no code implementations27 Mar 2024 Haitao Li, Qingyao Ai, Xinyan Han, Jia Chen, Qian Dong, Yiqun Liu, Chong Chen, Qi Tian

Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate relevance using textual semantic similarity.

Retrieval Semantic Similarity +2

Capability-aware Prompt Reformulation Learning for Text-to-Image Generation

1 code implementation27 Mar 2024 Jingtao Zhan, Qingyao Ai, Yiqun Liu, Jia Chen, Shaoping Ma

Our in-depth analysis of these logs reveals that user prompt reformulation is heavily dependent on the individual user's capability, resulting in significant variance in the quality of reformulation pairs.

Text-to-Image Generation

BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models

no code implementations27 Mar 2024 Haitao Li, Qingyao Ai, Jia Chen, Qian Dong, Zhijing Wu, Yiqun Liu, Chong Chen, Qi Tian

However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge essential for tasks in vertical domains, such as legal, medical, etc.

Bayesian Optimization

Scaling Laws For Dense Retrieval

no code implementations27 Mar 2024 Yan Fang, Jingtao Zhan, Qingyao Ai, Jiaxin Mao, Weihang Su, Jia Chen, Yiqun Liu

In this study, we investigate whether the performance of dense retrieval models follows the scaling law as other neural models.

Data Augmentation Retrieval +1

Improving Legal Case Retrieval with Brain Signals

no code implementations20 Mar 2024 Ruizhe Zhang, Qingyao Ai, Ziyi Ye, Yueyue Wu, Xiaohui Xie, Yiqun Liu

Traditional feedback signal such as clicks is too coarse to use as they do not reflect any fine-grained relevance information.

EEG Retrieval

Evaluation Ethics of LLMs in Legal Domain

no code implementations17 Mar 2024 Ruizhe Zhang, Haitao Li, Yueyue Wu, Qingyao Ai, Yiqun Liu, Min Zhang, Shaoping Ma

In recent years, the utilization of large language models for natural language dialogue has gained momentum, leading to their widespread adoption across various domains.


DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models

1 code implementation15 Mar 2024 Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu, Yiqun Liu

Our framework is specifically designed to make decisions on when and what to retrieve based on the LLM's real-time information needs during the text generation process.

Retrieval Sentence +1

Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models

no code implementations11 Mar 2024 Weihang Su, Changyue Wang, Qingyao Ai, Yiran Hu, Zhijing Wu, Yujia Zhou, Yiqun Liu

Hallucinations in large language models (LLMs) refer to the phenomenon of LLMs producing responses that are coherent yet factually inaccurate.


Gender Biased Legal Case Retrieval System on Users' Decision Process

no code implementations25 Feb 2024 Ruizhe Zhang, Qingyao Ai, Yiqun Liu, Yueyue Wu, Beining Wang

Gender of the defendants in both the task and relevant cases was edited to statistically measure the effect of gender bias in the legal case search results on participants' perceptions.


Query Augmentation by Decoding Semantics from Brain Signals

1 code implementation24 Feb 2024 Ziyi Ye, Jingtao Zhan, Qingyao Ai, Yiqun Liu, Maarten de Rijke, Christina Lioma, Tuukka Ruotsalo

If the quality of the initially retrieved documents is low, then the effectiveness of query augmentation would be limited as well.

Document Ranking

PRE: A Peer Review Based Large Language Model Evaluator

no code implementations28 Jan 2024 Zhumin Chu, Qingyao Ai, Yiteng Tu, Haitao Li, Yiqun Liu

Existing paradigms rely on either human annotators or model-based evaluators to evaluate the performance of LLMs on different tasks.

Language Modelling Large Language Model +1

Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval

1 code implementation17 Dec 2023 Weihang Su, Qingyao Ai, Xiangsheng Li, Jia Chen, Yiqun Liu, Xiaolong Wu, Shengluan Hou

With the development of deep learning and natural language processing techniques, pre-trained language models have been widely used to solve information retrieval (IR) problems.

Information Retrieval Retrieval +1

Light-weight CNN-based VVC Inter Partitioning Acceleration

no code implementations17 Dec 2023 Yiqun Liu, Mohsen Abdoli, Thomas Guionnet, Christine Guillemot, Aline Roumy

Compared to the High Efficiency Video Coding (HEVC) standard, VVC offers about 50% compression efficiency gain, in terms of Bjontegaard Delta-Rate (BD-rate), at the cost of about 10x more encoder complexity.

Relevance Feedback with Brain Signals

1 code implementation9 Dec 2023 Ziyi Ye, Xiaohui Xie, Qingyao Ai, Yiqun Liu, Zhihong Wang, Weihang Su, Min Zhang

To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based relevance feedback with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking.

Brain Computer Interface Re-Ranking

Language Generation from Brain Recordings

1 code implementation16 Nov 2023 Ziyi Ye, Qingyao Ai, Yiqun Liu, Maarten de Rijke, Min Zhang, Christina Lioma, Tuukka Ruotsalo

Inspired by recent research that revealed associations between the brain and the large computational language models, we propose a generative language BCI that utilizes the capacity of a large language model (LLM) jointly with a semantic brain decoder to directly generate language from functional magnetic resonance imaging (fMRI) input.

Language Modelling Large Language Model +2

Caseformer: Pre-training for Legal Case Retrieval Based on Inter-Case Distinctions

1 code implementation1 Nov 2023 Weihang Su, Qingyao Ai, Yueyue Wu, Yixiao Ma, Haitao Li, Yiqun Liu, Zhijing Wu, Min Zhang

Legal case retrieval aims to help legal workers find relevant cases related to their cases at hand, which is important for the guarantee of fairness and justice in legal judgments.

Fairness Retrieval

LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset

no code implementations26 Oct 2023 Haitao Li, Yunqiu Shao, Yueyue Wu, Qingyao Ai, Yixiao Ma, Yiqun Liu

However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling.

Fairness Retrieval

CNN-based Prediction of Partition Path for VVC Fast Inter Partitioning Using Motion Fields

2 code implementations20 Oct 2023 Yiqun Liu, Marc Riviere, Thomas Guionnet, Aline Roumy, Christine Guillemot

Experiments show that the proposed method can achieve acceleration ranging from 16. 5% to 60. 2% under the RandomAccess Group Of Picture 32 (RAGOP32) configuration with a reasonable efficiency drop ranging from 0. 44% to 4. 59% in terms of BD-rate, which surpasses other state-of-the-art solutions.

Investigating the Influence of Legal Case Retrieval Systems on Users' Decision Process

no code implementations7 Oct 2023 Beining Wang, Ruizhe Zhang, Yueyue Wu, Qingyao Ai, Min Zhang, Yiqun Liu

Given a specific query case, legal case retrieval systems aim to retrieve a set of case documents relevant to the case at hand.

Decision Making Information Retrieval +1

Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback

no code implementations29 Sep 2023 Qian Dong, Yiding Liu, Qingyao Ai, Zhijing Wu, Haitao Li, Yiqun Liu, Shuaiqiang Wang, Dawei Yin, Shaoping Ma

Large language models (LLMs) have demonstrated remarkable capabilities across various research domains, including the field of Information Retrieval (IR).

Data Augmentation Information Retrieval +4

GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network

1 code implementation27 Sep 2023 Kaiyuan Zhang, Ziyi Ye, Qingyao Ai, Xiaohui Xie, Yiqun Liu

Recognizing this shortfall, there has been a burgeoning interest in recent years in harnessing the potential of Graph Neural Networks (GNN) to exploit the topological information by modeling features selected from each EEG channel in a graph structure.

Classification EEG

An Intent Taxonomy of Legal Case Retrieval

no code implementations25 Jul 2023 Yunqiu Shao, Haitao Li, Yueyue Wu, Yiqun Liu, Qingyao Ai, Jiaxin Mao, Yixiao Ma, Shaoping Ma

Through a laboratory user study, we reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval.

Information Retrieval Retrieval +1

THUIR2 at NTCIR-16 Session Search (SS) Task

no code implementations1 Jul 2023 Weihang Su, Xiangsheng Li, Yiqun Liu, Min Zhang, Shaoping Ma

Our team(THUIR2) participated in both FOSS and POSS subtasks of the NTCIR-161 Session Search (SS) Task.

Language Modelling Learning-To-Rank +1

I^3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval

1 code implementation4 Jun 2023 Qian Dong, Yiding Liu, Qingyao Ai, Haitao Li, Shuaiqiang Wang, Yiqun Liu, Dawei Yin, Shaoping Ma

Moreover, the proposed implicit interaction is compatible with special pre-training and knowledge distillation for passage retrieval, which brings a new state-of-the-art performance.

Knowledge Distillation Passage Retrieval +2

CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding

no code implementations9 May 2023 Yixiao Ma, Yueyue Wu, Weihang Su, Qingyao Ai, Yiqun Liu

In the data sampling phase, we enhance the quality of the training data by utilizing fine-grained law article information to guide the selection of positive and negative examples.


A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning

1 code implementation28 Apr 2023 Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yiqun Liu, Yixing Fan, Xueqi Cheng

Learning task-specific retrievers that return relevant contexts at an appropriate level of semantic granularity, such as a document retriever, passage retriever, sentence retriever, and entity retriever, may help to achieve better performance on the end-to-end task.

Retrieval Sentence

THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank

1 code implementation25 Apr 2023 Jia Chen, Haitao Li, Weihang Su, Qingyao Ai, Yiqun Liu

This paper introduces the approaches we have used to participate in the WSDM Cup 2023 Task 1: Unbiased Learning to Rank.


Constructing Tree-based Index for Efficient and Effective Dense Retrieval

1 code implementation24 Apr 2023 Haitao Li, Qingyao Ai, Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Zheng Liu, Zhao Cao

Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance.

Contrastive Learning Retrieval

SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval

1 code implementation22 Apr 2023 Haitao Li, Qingyao Ai, Jia Chen, Qian Dong, Yueyue Wu, Yiqun Liu, Chong Chen, Qi Tian

Moreover, in contrast to the general retrieval, the relevance in the legal domain is sensitive to key legal elements.

Language Modelling Retrieval

Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank

no code implementations28 Feb 2023 Haitao Li, Jia Chen, Weihang Su, Qingyao Ai, Yiqun Liu

This paper describes the approach of the THUIR team at the WSDM Cup 2023 Pre-training for Web Search task.


Diverse legal case search

no code implementations29 Jan 2023 Ruizhe Zhang, Qingyao Ai, Yueyue Wu, Yixiao Ma, Yiqun Liu

In the process of searching, legal practitioners often need the search results under several different causes of cases as reference.

Retrieval Specificity

Brain Topography Adaptive Network for Satisfaction Modeling in Interactive Information Access System

1 code implementation17 Aug 2022 Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuesong Chen, Min Zhang, Shaoping Ma

We explore the effectiveness of BTA for satisfaction modeling in two popular information access scenarios, i. e., search and recommendation.

EEG Recommendation Systems +1

CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks

1 code implementation16 Aug 2022 Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yiqun Liu, Yixing Fan, Xueqi Cheng

We show that a strong generative retrieval model can be learned with a set of adequately designed pre-training tasks, and be adopted to improve a variety of downstream KILT tasks with further fine-tuning.


Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval

1 code implementation11 Aug 2022 Jingtao Zhan, Qingyao Ai, Yiqun Liu, Jiaxin Mao, Xiaohui Xie, Min Zhang, Shaoping Ma

By making the REM and DAMs disentangled, DDR enables a flexible training paradigm in which REM is trained with supervision once and DAMs are trained with unsupervised data.

Ad-Hoc Information Retrieval Domain Adaptation +1

HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle

1 code implementation12 Jul 2022 Guoxia Wang, Xiaomin Fang, Zhihua Wu, Yiqun Liu, Yang Xue, Yingfei Xiang, dianhai yu, Fan Wang, Yanjun Ma

Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to implement the training and inference of AlphaFold2 from scratch.

Protein Structure Prediction

Towards Representation Alignment and Uniformity in Collaborative Filtering

2 code implementations26 Jun 2022 Chenyang Wang, Yuanqing Yu, Weizhi Ma, Min Zhang, Chong Chen, Yiqun Liu, Shaoping Ma

Then, we empirically analyze the learning dynamics of typical CF methods in terms of quantified alignment and uniformity, which shows that better alignment or uniformity both contribute to higher recommendation performance.

Collaborative Filtering Recommendation Systems

A Survey on the Fairness of Recommender Systems

no code implementations8 Jun 2022 Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, Shaoping Ma

First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues.

Fairness Recommendation Systems

Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models

1 code implementation25 Apr 2022 Jingtao Zhan, Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation.


A Survey on Dropout Methods and Experimental Verification in Recommendation

no code implementations5 Apr 2022 Yangkun Li, Weizhi Ma, Chong Chen, Min Zhang, Yiqun Liu, Shaoping Ma, Yuekui Yang

Among various methods of coping with overfitting, dropout is one of the representative ways.

NxtPost: User to Post Recommendations in Facebook Groups

no code implementations8 Feb 2022 Kaushik Rangadurai, Yiqun Liu, Siddarth Malreddy, Xiaoyi Liu, Piyush Maheshwari, Vishwanath Sangale, Fedor Borisyuk

In this paper, we present NxtPost, a deployed user-to-post content-based sequential recommender system for Facebook Groups.

Sequential Recommendation

Interpreting Dense Retrieval as Mixture of Topics

no code implementations27 Nov 2021 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

Dense Retrieval (DR) reaches state-of-the-art results in first-stage retrieval, but little is known about the mechanisms that contribute to its success.


Web Search via an Efficient and Effective Brain-Machine Interface

no code implementations14 Oct 2021 Xuesong Chen, Ziyi Ye, Xiaohui Xie, Yiqun Liu, Weihang Su, Shuqi Zhu, Min Zhang, Shaoping Ma

While search technologies have evolved to be robust and ubiquitous, the fundamental interaction paradigm has remained relatively stable for decades.


Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

4 code implementations12 Oct 2021 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space.

Constrained Clustering Information Retrieval +3

Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search

no code implementations22 Sep 2021 Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuancheng Li, Jiaji Li, Xuesong Chen, Min Zhang, Shaoping Ma

Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i. e., content and context factors).


Towards a Better Understanding Human Reading Comprehension with Brain Signals

1 code implementation3 Aug 2021 Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuesong Chen, Min Zhang, Shaoping Ma

In this paper, we carefully design a lab-based user study to investigate brain activities during reading comprehension.

EEG Information Retrieval +4

Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance

5 code implementations2 Aug 2021 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

Compared with previous DR models that use brute-force search, JPQ almost matches the best retrieval performance with 30x compression on index size.

Information Retrieval Quantization +1

Optimizing Dense Retrieval Model Training with Hard Negatives

4 code implementations16 Apr 2021 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

ADORE replaces the widely-adopted static hard negative sampling method with a dynamic one to directly optimize the ranking performance.

Information Retrieval Representation Learning +1

SelfGait: A Spatiotemporal Representation Learning Method for Self-supervised Gait Recognition

1 code implementation27 Mar 2021 Yiqun Liu, Yi Zeng, Jian Pu, Hongming Shan, Peiyang He, Junping Zhang

In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, diverse, unlabeled gait data as a pre-training process to improve the representation abilities of spatiotemporal backbones.

Gait Recognition Representation Learning

Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently

2 code implementations20 Oct 2020 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma

Through this process, it teaches the DR model how to retrieve relevant documents from the entire corpus instead of how to rerank a potentially biased sample of documents.

Passage Retrieval Retrieval

Practical Deep Raw Image Denoising on Mobile Devices

1 code implementation ECCV 2020 Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, Jue Wang

Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets.

Efficient Neural Network Image Denoising

An Empirical Study of Clarifying Question-Based Systems

no code implementations1 Aug 2020 Jie Zou, Evangelos Kanoulas, Yiqun Liu

Search and recommender systems that take the initiative to ask clarifying questions to better understand users' information needs are receiving increasing attention from the research community.

Recommendation Systems

Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation

2 code implementations1 Jul 2020 Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma

However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG.

Knowledge Graph Embedding Knowledge Graphs +2

RepBERT: Contextualized Text Embeddings for First-Stage Retrieval

3 code implementations28 Jun 2020 Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma

Although exact term match between queries and documents is the dominant method to perform first-stage retrieval, we propose a different approach, called RepBERT, to represent documents and queries with fixed-length contextualized embeddings.

Passage Ranking Retrieval

STAS: Adaptive Selecting Spatio-Temporal Deep Features for Improving Bias Correction on Precipitation

no code implementations13 Apr 2020 Yiqun Liu, Shouzhen Chen, Lei Chen, Hai Chu, Xiaoyang Xu, Junping Zhang, Leiming Ma

We thus propose an end-to-end deep-learning BCoP model named Spatio-Temporal feature Auto-Selective (STAS) model to select optimal ST regularity from EC via the ST Feature-selective Mechanisms (SFM/TFM).

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

1 code implementation9 Mar 2019 Weizhi Ma, Min Zhang, Yue Cao, Woojeong, Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, Xiang Ren

The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue.

Explainable Recommendation Knowledge Graphs +1

Temporal Relational Ranking for Stock Prediction

3 code implementations25 Sep 2018 Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat-Seng Chua

Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.

Relation Network Stock Prediction +1

Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover's Distance Regularization

no code implementations COLING 2016 Meng Zhang, Yang Liu, Huanbo Luan, Yiqun Liu, Maosong Sun

Being able to induce word translations from non-parallel data is often a prerequisite for cross-lingual processing in resource-scarce languages and domains.

Translation Word Alignment +1

Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification

no code implementations11 Feb 2015 Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma

In this paper, we focus on the problem of phrase-level sentiment polarity labelling and attempt to bridge the gap between phrase-level and review-level sentiment analysis.

Classification General Classification +2

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