108 research outputs found
Analysis of Effector T Lymphocyte Subpopulations Necessary to Mediate Semisyngeneic Tumor Rejection in Mice
No full textCoarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models
No full textFine-tuning in information retrieval systems using pre-trained language
models (PLM-based IR) requires learning query representations and
query-document relations, in addition to downstream task-specific learning.
This study introduces coarse-tuning as an intermediate learning stage that
bridges pre-training and fine-tuning. By learning query representations and
query-document relations in coarse-tuning, we aim to reduce the load of
fine-tuning and improve the learning effect of downstream IR tasks. We propose
Query-Document Pair Prediction (QDPP) for coarse-tuning, which predicts the
appropriateness of query-document pairs. Evaluation experiments show that the
proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc
document retrieval datasets. Furthermore, the results of the query prediction
task suggested that coarse-tuning facilitated learning of query representation
and query-document relations.Comment: Accepted at LREC-COLING 202
Analysis of Relevant Text Fragments for Different Search Task Types
No full textThis paper investigates the trend of relevant text fragments by task type. The search results of _ne-grained information retrieval systems propose not documents but text fragments. We hypothesize that the properties of relevant text fragments depend on the task type. To reveal these properties, we evaluate a relevant text fragment to judge (1) its granularity (e.g., word, phrase, or sentence) and (2) its structural complexity. Our analysis shows that a task type based on more complex information needs has a larger granularity of relevant text fragments. On the other hand, the complexity of task type's information needs does not necessarily correlate with the structural complexity of the relevant text fragments
Part-of-speech Tagging for Web Search Queries Using a Large-scale Web Corpus
No full textThis paper proposes an accurate part-of-speech (POS) tagging method for Web search queries using the sentence level morphological analysis results of a large-scale Web corpus. POS tagging is a fundamental technique for analyzing queries; however, the existing NLP tools often fail to correctly identify POS tags because queries are not based on natural language grammar. We propose a method not affected by the queries' characteristics lacking capitalization and free word order with the term-POS database (TPDB). Experimental results show that the proposed method significantly outperforms those using existing NLP tools and the state-of-the-art method. In addition, the data set we created is expected to be useful for future researches on both POS tagging systems to queries and IR systems leveraging POS tags
ditlab at the NTCIR-16 QA Lab-PoliInfo-3
No full textThe ditlab team participated in the QA alignment and Question Answering task of the NTCIR-16 QA Lab-PoliInfo-3 task. First, we developed a QA alignment system that associates each question to its answer by using heuristic rules to make paragraphs composed of the related sentences and by matching them. Heuristic rules were optimized for minutes. We prepared four types of features for matching. Second, we built a QA system that uses a similarity measure to find the original question similar to the question summary. The QA system then identified the answers associated with the original question using the results of the QA alignment described above. A Text-to-Text Transfer Transformer (T5) was used to summarize the associated answer.conference pape
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