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TMUNLP at the NTCIR-17 FinArg-1 Task
Get PDFThe TMUNLP team participated in the FinArg-1 Task of NTCIR- 17, focusing on Argument Unit Identification and Argument Relation Identification in the finance domain using social media and earnings call datasets. Notably, the team ranked 1st and 3rd in these subtasks, respectively. This paper presents the team's methodologies, results, and conclusions. For Earning Conference Call (ECC) Argument Unit Identification, an ensemble strategy combining diverse pre-trained models achieved a Macro F1 score of 0.766231, with significant contributions from models like ELECTRA, RoBERTa, BERT-base-uncased, and FinBERT. In ECC Argument Relation Identification, a combination of pretrained models and sampling strategies, along with voting mechanisms, improved natural language inference tasks. Future research opportunities include optimizing integration methods for semantic inference efficiency. Finally, in Social Media (SM) Argument Relation Identification, ChatGPT's keyword features positively impacted model performance. Challenges of translation and data imbalance were addressed through category-weighted sampling methods and soft voting, showcasing adaptable strategies. This study highlights the efficacy of ensemble strategies and diverse models in NLP tasks and emphasizes potential advancements in the field.conference pape
ditlab at the NTCIR-17 Transfer Task
Get PDFThe ditlab team participated in the Transfer task composed of dense retrieval and dense reranking subtasks. We trained sentence-BERT by using a Japanese version of mMARCO dataset and commonly used for both subtasks. We compared three types of models that were trained according to three types of losses: softmax, triplet, multiple negatives ranking losses. The results show that the multiple negatives ranking loss was the best for both subtasks. In addition, system fusions significantly improved the performance especially for the retrieval task.conference pape
STIS at the NTCIR-17 MedNLP-SC Task: Incorporating Sentiment to Transformer Architecture for Adverse Drug Event Detection on Social Media
Get PDFThis paper presents the system and results of the STIS team for the Social Media (English) subtasks of the NTCIR-17 MedNLP-SC Task. We proposed incorporating the sentiment of social media texts into a pre-trained Transformer model in detecting adverse drug events on social media. A lexicon-based and rule-based sentiment analysis VADER model was used to predict each tweet sentiment. Based on the experimental results of the ADE vs. non-ADE binary classification task, our proposed fine-tuned model outperformed the baseline by a slight difference. Specifically, our model achieves a better F1 score for 9 of 22 symptoms in the symptom detection task.conference pape
Overview of the NTCIR-17 Session Search (SS-2) Task
Get PDFThis is an overview of the NTCIR-17 Session Search (SS-2) task. The task features the Fully Observed Session Search subtask (FOSS), the Partially Observed Session Search subtask (POSS) and the Session-level Search Effectiveness Estimation subtask(SSEE). This year, we received 16 runs from 2 teams in total. This paper will describe the task background, data, subtasks, evaluation measures, and the evaluation results, respectively.conference pape
KANDUH at the NTCIR-17 Transfer Task
Get PDFThe KANDUH team participated in the Transfer subtasks 1 and 2 of NTCIR-17.In this paper, we report on our approach to solving the problem and the results.Subtasks 1 and 2 address the dense vector search task, respectively.In both subtasks 1 and 2, we used BM25 to filter documents, followed by dense vector retrieval.The method with the highest nDCG@20 was 0.4339 the one that first finetuned DeBERTa-v2 with MSMARCO and then additionally finetuned with NTCIR-1 data.On the other hand, the method with the lowest nDCG@20 was 0.0751 the one that fine-tuned only MSMARCO data.conference pape
Interactive Sub-Task Focus: LifeInsight’s Contribution to NTCIR-17 Lifelog-5
Get PDFThe rise of digital storage technology and portable sensors has led to an increase in lifelogging, where individuals digitally record their personal experiences. This has opened up new research opportunities in lifelog data retrieval. However, the real-time and automatic recording of data by sensors presents unique challenges compared to traditional search engines, particularly in data organization and search. The highly personalized nature of the dataset also necessitates the consideration of user interactions and feedback in the search engine. In this paper, we present LifeInsight, a robust lifelog retrieval system designed specifically for the NTCIR17 Lifelog-5 Task. Originally developed for the Lifelog Search Challenge (LSC), the system has been adapted and optimized to address the unique requirements of the Lifelog Semantic Access Task (LSAT). Of the two tasks within NTCIR17 Lifelog-5, our primary focus is on the interactive sub-task, which involves evaluating LifeInsight's performance under different user interaction approaches employed by various users. Therefore, a comprehensive user study was conducted to evaluate the LifeInsight system encompassed both expert and novice users across various settings, including ad-hoc and known-item-search scenarios.conference pape
Heaps’ Law in GPT-Neo Large Language Model Emulated Corpora
Get PDFHeaps’ law is an empirical relation in text analysis that predicts vocabulary growth as a function of corpus size. While this law has been validated in diverse human-authored text corpora, its applicability to large language model generated text remains unexplored. This study addresses this gap, focusing on the emulation of corpora using the suite of GPT-Neo large language models. To conduct our investigation, we emulated corpora of PubMed abstracts using three different parameter sizes of the GPT-Neo model. Our emulation strategy involved using the initial five words of each PubMed abstract as a prompt and instructing the model to expand the con- tent up to the original abstract’s length. Our findings indicate that the generated corpora adhere to Heaps’ law. Interestingly, as the GPT-Neo model size grows, its generated vocabulary increasingly adheres to Heaps’ law as as observed in human-authored text. To further improve the richness and authenticity of GPT-Neo outputs, future iterations could emphasize enhancing model size or refining the model architecture to curtail vocabulary repetition.conference pape
ダイ 38 カイ コレカラ ノ ガクジュツ ジョウホウ システム コウチク ケントウ イインカイ ハイフシリョウ
Get PDF会議名：第38回 これからの学術情報システム構築検討委員会
開催場所：オンライン
日時：2023年12月6日（水）14:30～16:30conference outpu
