从容
发表于 2025-3-23 12:23:50
Final Report of the NTCIR-14 OpenLiveQ-2 Task multileaving method that showed high efficiency over the other methods in a recent study. We describe the details of the task, data, and evaluation methods, and then report official results at NTCIR-14 OpenLiveQ-2. Furthermore, we demonstrate the effectiveness and efficiency of the proposed evaluation methodology.
Carminative
发表于 2025-3-23 15:20:43
Team’s Summarization System at the NTCIR-14 QA Lab-PoliInfo-sized data sets step by step. As a result, our system achieved good performance, especially in the evaluation by ROUGE scores. In this paper, we also compare our system with a single random forest classifier using probability.
oblique
发表于 2025-3-23 18:20:21
THUIR at the NTCIR-14 WWW-2 Taskish subtasks. Through further analysis of results, we find that our neural models can achieve better performances in all navigational, informational and transactional queries in Chinese subtask. In the English subtask, the learning-to-rank methods have stronger modeling capabilities than BM25 by learning from effective hand-crafted features.
半导体
发表于 2025-3-24 00:26:44
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JAMB
发表于 2025-3-24 05:17:54
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Gudgeon
发表于 2025-3-24 07:47:06
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scotoma
发表于 2025-3-24 11:56:13
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不理会
发表于 2025-3-24 15:10:49
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Haphazard
发表于 2025-3-24 20:19:47
Online Evaluations of Features and Ranking Models for Question Retrieval25F-like features and translation-based features in addition to basic features such as TF, TFIDF, and BM25 and then constructed multiple ranking models with the feature sets. In the first stage of online evaluation, our linear models with the BM25F-like and translation-based features obtained the hi
originality
发表于 2025-3-25 00:35:13
Studying Online and Offline Evaluation Measures: A Case Study Based on the NTCIR-14 OpenLiveQ-2 Taskheir answers, participants in the OpenLiveQ task were required to return a ranked list of questions that potentially match and satisfy the user’s query effectively. In this paper we focus on two main investigations: (i) Finding effective features which go beyond only-relevance for the task of rankin