10 citations · 12 across the 10 of their papers we have counts for
11 papers
Understanding Interpretation Difficulty in Harmful Online Communication: Insights from Cybercrime Communities
Tomohiro Okatsu, Naoki Takada, Yin Min Pa Pa +2
Harmful online communication often contains slang, coded terms, abbreviations, and community-specific expressions, which make messages difficult to interpret. This paper presents a…
APO-DD: Dialogue Disentanglement via Reflective Optimization of Multi-Component Prompts
Naoki Takada, Tatsunori Mori, Tomohiro Okatsu +2
In multi-party chat, interleaved dialogues make it difficult to identify the context of each utterance. Dialogue disentanglement separates these dialogues to support dialogue analy…
GuideWeb: A Benchmark for Automatic In-App Guide Generation on Real-World Web UIs
Chengguang Gan, Yoshihiro Tsujii, Yunhao Liang +3
Digital Adoption Platform (DAP) provide web-based overlays that deliver operation guidance and contextual hints to help users navigate complex websites. Although modern DAP tools e…
M-MRE: Extending the Mutual Reinforcement Effect to Multimodal Information Extraction
Chengguang Gan, Zhixi Cai, Yanbin Wei +3
Mutual Reinforcement Effect (MRE) is an emerging subfield at the intersection of information extraction and model interpretability. MRE aims to leverage the mutual understanding be…
Empirical Study of Mutual Reinforcement Effect and Application in Few-shot Text Classification Tasks via Prompt
Chengguang Gan, Tatsunori Mori
The Mutual Reinforcement Effect (MRE) investigates the synergistic relationship between word-level and text-level classifications in text classification tasks. It posits that the p…
A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction
Chengguang Gan, Sunbowen Lee, Qingyu Yin +9
The Mutual Reinforcement Effect (MRE) describes a phenomenon in information extraction where word-level and sentence-level tasks can mutually improve each other when jointly modele…
Demonstrating Mutual Reinforcement Effect through Information Flow
Chengguang Gan, Xuzheng He, Qinghao Zhang +1
The Mutual Reinforcement Effect (MRE) investigates the synergistic relationship between word-level and text-level classifications in text classification tasks. It posits that the p…
Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening
Chengguang Gan, Qinghao Zhang, Tatsunori Mori
The automation of resume screening is a crucial aspect of the recruitment process in organizations. Automated resume screening systems often encompass a range of natural language p…
Think from Words(TFW): Initiating Human-Like Cognition in Large Language Models Through Think from Words for Japanese Text-level Classification
Chengguang Gan, Qinghao Zhang, Tatsunori Mori
The proliferation of Large Language Models (LLMs) has spurred extensive research into LLM-related Prompt investigations, such as Instruction Learning (IL), In-context Learning (ICL…
GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect
Chengguang Gan, Qinghao Zhang, Tatsunori Mori
Information Extraction (IE) stands as a cornerstone in natural language processing, traditionally segmented into distinct sub-tasks. The advent of Large Language Models (LLMs) hera…
USA: Universal Sentiment Analysis Model & Construction of Japanese Sentiment Text Classification and Part of Speech Dataset
Chengguang Gan, Qinghao Zhang, Tatsunori Mori
Sentiment analysis is a pivotal task in the domain of natural language processing. It encompasses both text-level sentiment polarity classification and word-level Part of Speech(PO…