Information Extraction (IE) stands as a cornerstone in natural language processing, traditionally segmented into distinct sub-tasks. The advent of Large Language Models (LLMs) heralds a paradigm shift, suggesting the feasibility of a singular model addressing multiple IE subtasks. However, the efficacy of employing LLMs directly trained on chat-based data for IE tasks is considerably subpar when juxtaposed with conventional methods employed in prior studies. In order to address this limitation and harness the robust generalization capabilities inherent in LLMs, we propose the General Information Extraction Large Language Model (GIELLM). GIELLM seamlessly integrates various tasks, including Text Classification, Sentiment Analysis, Named Entity Recognition, Relation Extraction, and Event Extraction, employing a unified input-output schema. This innovation marks the first instance of a model simultaneously handling such a diverse array of IE subtasks. Notably, the GIELLM leverages the Mutual Reinforcement Effect (MRE), enhancing performance in integrated tasks compared to their isolated counterparts. Our experiments demonstrate State-of-the-Art (SOTA) results in five out of six Japanese mixed datasets, significantly surpassing GPT-3.5-Turbo. Further, an independent evaluation using the novel Text Classification Relation and Event Extraction(TCREE) dataset corroborates the synergistic advantages of MRE in text and word classification. This breakthrough paves the way for most IE subtasks to be subsumed under a singular LLM framework. Specialized fine-tune task-specific models are no longer needed.

In the product development process, test planning can be a time-consuming task, in particular, the test methods for product reliability are usually selected manually because many documents must be referred to when selecting suitable test methods. To make this process more efficient, we have been researched methods to support test planning. In this paper a new automatic selection method for test plans is proposed. The proposed method makes use of information pertaining to cases of failure. We describe a method for searching for failure cases among products having similar attributes for a product currently under consideration. The main principle of this method consists in automated acquisition of names describing the product's constituent components for use in the search process. Experimental results show that the proposed method delivers a greater degree of acquisition performance, precision, and recall in acquiring names of components compared with a previously proposed method.

In this paper, we propose a method of non-factoid Web question-answering that can uniformly deal with any class of Japanese non-factoid question by using a large number of example Q&A pairs. Instead of preparing classes of questions beforehand, the method retrieves already asked question examples similar to a submitted question from a set of Q&A pairs. Then, instead of preparing clue expressions for the writing style of answers according to each question class beforehand, it dynamically extracts clue expressions from the answer examples corresponding to the retrieved question examples. This clue expression information is combined with topical content information from the question to extract appropriate answer candidates. The experimental results showed that the clue expressions obtained from the set of examples improved the accuracy of answer candidate extraction.

We have proposed a method to introduce A* search control in a sentential matching mechanism for Japanese question-answering systems in order to reduce the turnaround time while maintaining the accuracy of the answers. Using this method, preprocessing need not be performed on a document database and we may use any information retrieval systems by writing a simple wrapper program. However, the disadvantage is that the accuracy is not sufficiently high and the mean reciprocal rank (MRR) is approximately 0.3 in NTCIR3 QAC1, an evaluation workshop for question-answering systems. In order to improve the accuracy, we propose several measures of the degree of sentence matching and a variant of a voting method. Both of them can be integrated with our system of controlled search. Using these techniques, the system achieves a higher MRR of 0.5 in the evaluation workshop NTCIR4 QAC2.

In recent years, answer-focused summarization has gained attention as a technology complementary to information retrieval and question answering. In order to realize multi-document summarization focused by multiple questions, we propose a method to calculate sentence importance using scores, for responses to multiple questions, generated by a Question-Answering engine. Further, we describe the integration of this method with a generic multi-document summarization system. The evaluation results demonstrate that the performance of the proposed method is better than not only several baselines but also other participants' systems at the evaluation workshop NTCIR4 TSC3 Formal Run. However, it should be noted that some of the other systems do not use the information of questions.

article Share on Preface to the special issues on NTCIR-4 Editors: Hiroshi Nakagawa University of Tokyo University of TokyoView Profile , Tatsunori Mori Yokohama National University Yokohama National UniversityView Profile , Noriko Kando National Institute of Informatics National Institute of InformaticsView Profile Authors Info & Claims ACM Transactions on Asian Language Information ProcessingVolume 4Issue 3September 2005 pp 237–242https://doi.org/10.1145/1111667.1111668Published:01 September 2005Publication History 1citation288DownloadsMetricsTotal Citations1Total Downloads288Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access