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SPARC Japan Seminar 2023 "Preparing for Immediate OA: A Reintroduction to Licensing for Getting Your Papers and Data Used" Publisher perspectives on open access and licensing Document
Get PDFSPARC Japan セミナー2023「即時OAに備えて：論文・データを「つかってもらう」ためのライセンス再入門」
開催場所：オンライン開催
日時：2023年11月28日（火）13:00～17:00conference objec
第25回大学図書館と国立情報学研究所との連携・協力推進会議議事次第
Get PDF会議名：第25回大学図書館と国立情報学研究所との連携・協力推進会議
開催場所：オンライン
日時：2023年3月9日（木）13:00～15:00conference objec
ダイ 36 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 ギジヨウシ
Get PDF会議名：第36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6月23日（金）15:00～17:00conference outpu
LIPI at the NTCIR-17 FinArg-1 Task: Using Pre-trained Language Models for Comprehending Financial Arguments
Get PDFComprehending arguments from financial texts helps investors in making data driven decisions. The FinArg tasks of NTCIR-17 deal with mining arguments related to finance from Research Reports, Earnings Conference Calls, and Social Media. In this paper, we describe our team's approach to solve the three such problems - Argument Unit Classification, Argument Relation Detection & Classification, and Identifying Attack and Support Argumentative Relations. We obtained best performance using pre-trained language models (like BERT-SEC and FinBERT) and cross-encoder architecture.conference pape
CYUT at the NTCIR-17 FinArg-1 Task2: A Quantitative Prompt Engineering Approach for Identifying Attack and Support Argumentative Relations in Social Media Discussion Threads
Get PDFThis paper reports our prompt engineering approach to the FinArg-1 task. In year 2023, we focus on task 2. Our system adopts the GPT3.5 generation model to evaluate the argumentative relations in social media discussion threads. We used three different prompts guide the GPT3.5 model to evaluate the degree of support or attack, we refer it as a quantitative approach. Our system then collected the score to make the final decision. The official results shows promising direction of using quantitative prompt engineering on argumentative relation identification.conference pape
Embedding Tables in Text Context: A New Approach to UFO Tasks
Get PDFText-to-Table Relationship Extraction (TTRE)[3] has emerged as a significant research topic. Although tables enable humans to com- prehend complex data structures quickly, machines often struggle with such interpretations. The primary challenge of this paper lies in understanding the myriad intentions behind the table’s creation and the possible ambiguity when viewed without context. We pr pose an approach to address these issues by embedding a table in a textual context. Specifically, we convert tables contained in HTML- formatted documents to the Markdown format and create training data that combine the tables with information about the associated question text and elements. Then, we use the training data to train a QLoRA model based on llama2-13b-chat-hf. This approach promotes holistic interpretation of tables and their associated texts within a single vector space.conference pape