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1,088 results on '"Li, Jianling"'

2. LLM-enhanced Self-training for Cross-domain Constituency Parsing

Author

Li, Jianling, Zhang, Meishan, Guo, Peiming, Zhang, Min, and Zhang, Yue

Subjects
Computer Science - Computation and Language
Abstract

Self-training has proven to be an effective approach for cross-domain tasks, and in this study, we explore its application to cross-domain constituency parsing. Traditional self-training methods rely on limited and potentially low-quality raw corpora. To overcome this limitation, we propose enhancing self-training with the large language model (LLM) to generate domain-specific raw corpora iteratively. For the constituency parsing, we introduce grammar rules that guide the LLM in generating raw corpora and establish criteria for selecting pseudo instances. Our experimental results demonstrate that self-training for constituency parsing, equipped with an LLM, outperforms traditional methods regardless of the LLM's performance. Moreover, the combination of grammar rules and confidence criteria for pseudo-data selection yields the highest performance in the cross-domain constituency parsing., Comment: Accepted by EMNLP 2023 main conf

Published
2023

13. Word Graph Guided Summarization for Radiology Findings

Author

Hu, Jinpeng, Li, Jianling, Chen, Zhihong, Shen, Yaling, Song, Yan, Wan, Xiang, and Chang, Tsung-Hui

Subjects
Computer Science - Computation and Language
Abstract

Radiology reports play a critical role in communicating medical findings to physicians. In each report, the impression section summarizes essential radiology findings. In clinical practice, writing impression is highly demanded yet time-consuming and prone to errors for radiologists. Therefore, automatic impression generation has emerged as an attractive research direction to facilitate such clinical practice. Existing studies mainly focused on introducing salient word information to the general text summarization framework to guide the selection of the key content in radiology findings. However, for this task, a model needs not only capture the important words in findings but also accurately describe their relations so as to generate high-quality impressions. In this paper, we propose a novel method for automatic impression generation, where a word graph is constructed from the findings to record the critical words and their relations, then a Word Graph guided Summarization model (WGSum) is designed to generate impressions with the help of the word graph. Experimental results on two datasets, OpenI and MIMIC-CXR, confirm the validity and effectiveness of our proposed approach, where the state-of-the-art results are achieved on both datasets. Further experiments are also conducted to analyze the impact of different graph designs to the performance of our method., Comment: 11 pages, 6 figures, ACL2021 Findings

Published
2021

25. Long‐term efficacy of first‐line ibrutinib treatment for chronic lymphocytic leukaemia in patients with TP53 aberrations: a pooled analysis from four clinical trials

Author

Allan, John N, Shanafelt, Tait, Wiestner, Adrian, Moreno, Carol, O’Brien, Susan M, Li, Jianling, Krigsfeld, Gabriel, Dean, James P, and Ahn, Inhye E

Subjects
Clinical Trials and Supportive Activities, Clinical Research, Rare Diseases, Hematology, Cancer, Lymphoma, 6.2 Cellular and gene therapies, Evaluation of treatments and therapeutic interventions, Adenine, Adult, Aged, Aged, 80 and over, Clinical Trials as Topic, Female, Humans, Leukemia, Lymphocytic, Chronic, B-Cell, Male, Middle Aged, Piperidines, Tumor Suppressor Protein p53, chronic lymphocytic leukaemia, ibrutinib, TP53 mutation, del(17p), first-line, Cardiorespiratory Medicine and Haematology, Immunology
Abstract

TP53 aberrations [del(17p) or TP53 mutation] predict poor survival with chemoimmunotherapy in patients with chronic lymphocytic leukaemia (CLL). We evaluated long-term efficacy and safety of first-line ibrutinib-based therapy in patients with CLL bearing TP53 aberrations in a pooled analysis across four studies: PCYC-1122e, RESONATE-2 (PCYC-1115/16), iLLUMINATE (PCYC-1130) and ECOG-ACRIN E1912. The pooled analysis included 89 patients with TP53 aberrations receiving first-line treatment with single-agent ibrutinib (n = 45) or ibrutinib in combination with an anti-CD20 antibody (n = 44). All 89 patients had del(17p) (53% of 89 patients) and/or TP53 mutation (91% of 58 patients with TP53 sequencing results available). With a median follow-up of 49·8 months (range, 0·1-95·9), median progression-free survival was not reached. Progression-free survival rate and overall survival rate estimates at four years were 79% and 88%, respectively. Overall response rate was 93%, including complete response in 39% of patients. No new safety signals were identified in this analysis. Forty-six percent of patients remained on ibrutinib treatment at last follow-up. With median follow-up of four years (up to eight years), results from this large, pooled, multi-study data set suggest promising long-term outcomes of first-line ibrutinib-based therapy in patients with TP53 aberrations. Registered at ClinicalTrials.gov (NCT01500733, NCT01722487, NCT02264574 and NCT02048813).

Published
2022

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