Empathic Multiple Tutoring Agents for Multiple Learner Interface

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Generating Dialogues for Virtual Agents Using Nested Textual Coherence Relations

This paper describes recent advances on the Text2Dialogue system we are currently developing. Our system enables automatic transformation of monological text into a dialogue. The dialogue is then 'acted out' by virtual agents, using synthetic speech and gestures. In this paper, we focus on the monologue-to-dialogue transformation, and describe how it uses textual coherence relations to map text segments to query–answer pairs between an expert and a layman agent. By creating mapping rules for a few well-selected relations, we can produce coherent dialogues with proper assignment of turns for the speakers in a majority of cases

HILDA: A Discourse Parser Using Support Vector Machine Classification

Discourse structures have a central role in several computational tasks, such as question-answering or dialogue generation. In particular, the framework of the Rhetorical Structure Theory (RST) offers a sound formalism for hierarchical text organization. In this article, we present HILDA, an implemented discourse parser based on RST and Support Vector Machine (SVM) classification. SVM classifiers are trained and applied to discourse segmentation and relation labeling. By combining labeling with a greedy bottom-up tree building approach, we are able to create accurate discourse trees in linear time complexity. Importantly, our parser can parse entire texts, whereas the publicly available parser SPADE (Soricut and Marcu 2003) is limited to sentence level analysis. HILDA outperforms other discourse parsers for tree structure construction and discourse relation labeling. For the discourse parsing task, our system reaches 78.3% of the performance level of human annotators. Compared to a state-of-the-art rule-based discourse parser, our system achieves a performance increase of 11.6%

Recovery of Cryo-injured Rabbit Urethras by Biofabricated C-shaped Adipose-derived Mesenchymal Cell Structures

Article信州医学雑誌 68(6): 357-370(2020)journal articl

Staging the tumor and staging the host: A two centre, two country comparison of systemic inflammatory responses of patients undergoing resection of primary operable colorectal cancer

Background: How systemic inflammation-based prognostic scores such as the modified Glasgow Prognostic Score (mGPS) and neutrophil:lymphocyte ratio (NLR) differ across populations of patients with colorectal cancer (CRC) remains unknown. The present study examined the mGPS and NLR in patients from United Kingdom (UK) and Japan. Methods: Patients undergoing resection of TNM I-III CRC in two centres in the UK and Japan were included. Differences in clinicopathological characteristics and mGPS (0-CRP≤10 mg/L, 1-CRP>10 mg/L, 2-CRP>10 mg/L, albumin<35 g/L) and NLR (≤5/>5) were examined. Results: Patients from UK (n = 581) were more likely to be female, high ASA and BMI, present as an emergency (all P < 0.01) and have higher T stage compared to those from Japan (n = 559). After controlling for differences in tumor and host characteristics, patients from Japan were less likely to be systemically inflamed (OR: mGPS: 0.37, 95%CI 0.27–0.50, P < 0.001; NLR: 0.53, 95%CI 0.35–0.79, P = 0.002). Conclusion: Systemic inflammatory responses differ between populations with colorectal cancer. Given their prognostic value, reporting of systemic inflammation-based scores should be incorporated into future studies reporting patient outcomes. Summary: Although the systemic inflammatory response is recognised as a prognostic factor in patients with colorectal cancer, it is not clear how these may differ between distinct geographical populations. The present study examines differences in the prevalence of elevated systemic inflammatory responses (modified Glasgow Prognostic Score and neutrophil:lymphocyte ratio) between two populations undergoing resection of colorectal cancer in the United Kingdom and Japan