6 results on '"Huang, Jimmy Xiangji"'
Search Results
2. Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization.
- Author
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Laskar, Md Tahmid Rahman, Hoque, Enamul, and Huang, Jimmy Xiangji
- Subjects
NATURAL language processing ,SUPERVISED learning ,TEXT summarization - Abstract
The Query-Focused Text Summarization (QFTS) task aims at building systems that generate the summary of the text document(s) based on the given query. A key challenge in addressing this task is the lack of large labeled data for training the summarization model. In this article, we address this challenge by exploring a series of domain adaptation techniques. Given the recent success of pre-trained transformer models in a wide range of natural language processing tasks, we utilize such models to generate abstractive summaries for the QFTS task for both single-document and multi-document scenarios. For domain adaptation, we apply a variety of techniques using pre-trained transformer-based summarization models including transfer learning, weakly supervised learning, and distant supervision. Extensive experiments on six datasets show that our proposed approach is very effective in generating abstractive summaries for the QFTS task while setting a new state-of-the-art result in several datasets across a set of automatic and human evaluation metrics. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. Modeling and Learning Distributed Word Representation with Metadata for Question Retrieval.
- Author
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Zhou, Guangyou and Huang, Jimmy Xiangji
- Subjects
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METADATA , *FACILITATED learning , *INTERROGATIVE (Grammar) , *DOCUMENT type definitions , *ARCS Model of Motivational Design - Abstract
Community question answering (cQA) has become an important issue due to the popularity of cQA archives on the Web. This paper focuses on addressing the lexical gap problem in question retrieval. Question retrieval in cQA archives aims to find the existing questions that are semantically equivalent or relevant to the queried questions. However, the lexical gap problem brings a new challenge for question retrieval in cQA. In this paper, we propose to model and learn distributed word representations with metadata of category information within cQA pages for question retrieval using two novel category powered models. One is a basic category powered model called MB-NET and the other one is an enhanced category powered model called ME-NET which can better learn the distributed word representations and alleviate the lexical gap problem. To deal with the variable size of word representation vectors, we employ the framework of fisher kernel to transform them into the fixed-length vectors. Experimental results on large-scale English and Chinese cQA data sets show that our proposed approaches can significantly outperform state-of-the-art retrieval models for question retrieval in cQA. Moreover, we further conduct our approaches on large-scale automatic evaluation experiments. The evaluation results show that promising and significant performance improvements can be achieved. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
4. Incorporating rich features to boost information retrieval performance: A SVM-regression based re-ranking approach
- Author
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Ye, Zheng, Huang, Jimmy Xiangji, and Lin, Hongfei
- Subjects
- *
INFORMATION retrieval , *STATISTICAL weighting , *RANKING , *HEURISTIC algorithms , *SUPPORT vector machines , *REGRESSION analysis , *QUERY (Information retrieval system) , *NATURAL language processing - Abstract
Abstract: Document ranking is an essential problem in the field of information retrieval (IR). Traditional weighting models such as BM25 and Language model can only take advantage of query terms. IR is a complex process that may be affected by a series of heterogeneous features. It is necessary to refine first-pass retrieval results by taking rich features into account. Traditional heuristic re-ranking approaches can only take advantage of a small number of homogeneous features that may affect information retrieval performance. In this paper, we propose and evaluate a regression-based document re-ranking approach for IR, in which we use SVM regression model to learn a re-ranking function automatically. Under this regression-based framework, we can take advantage of rich features to re-rank the firs-pass retrieved documents by traditional weighting models. We conduct a series of experiments on four standard IR collections in two different languages. The experimental results show that our proposed approach can significantly improve the retrieval performance over the first-pass retrieval. Moreover, by refining the first-pass retrieved document set, the traditional pseudo relevant feedback approaches can also be enhanced. [Copyright &y& Elsevier]
- Published
- 2011
- Full Text
- View/download PDF
5. GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings.
- Author
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Xie, Zhiwen, Zhu, Runjie, Liu, Jin, Zhou, Guangyou, Huang, Jimmy Xiangji, and Cui, Xiaohui
- Subjects
- *
KNOWLEDGE graphs , *ARTIFICIAL intelligence , *COVID-19 pandemic , *COVID-19 , *MACHINE learning - Abstract
In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic relations which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with different scoring functions to learn entity and relation embeddings published. However, these models share the same problems of rarely taking important features of KG like attribute features, other than relation triples, into account, while dealing with the heterogeneous, complex and incomplete COVID-19 medical data. To address the above issue, we propose a graph feature collection network (GFCNet) for COVID-19 KGE task, which considers both neighbor and attribute features in KGs. The extensive experiments conducted on the COVID-19 drug KG dataset show promising results and prove the effectiveness and efficiency of our proposed model. In addition, we also explain the future directions of deepening the study on COVID-19 KGE task. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. DFM: A parameter-shared deep fused model for knowledge base question answering.
- Author
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Zhou, Guangyou, Xie, Zhiwen, Yu, Zongfu, and Huang, Jimmy Xiangji
- Subjects
- *
KNOWLEDGE base , *NATURAL language processing , *MULTILEVEL models - Abstract
Currently, Knowledge Base Question Answering (KBQA) is an important research topic in the fields of information retrieval (IR) and natural language processing (NLP). The most common questions asked on the Web are simple questions, which can be answered by a single relational fact in a knowledge base (KB). However, answering simple questions automatically remains a challenging task in the IR and NLP research communities. Based on a review of various studies and a detailed analysis, we surmise that these challenges are primarily related to the following concerns: (1) how to effectively access a large-scale KB; and (2) how to effectively reduce the gap between NL questions and the structured semantics in a KB. Most previous studies have considered these as separate and independent subtasks, subject detection and predicate matching. Here, we propose a deep fused model that combines subject detection and predicate matching under a unified framework. Specifically, we employ a subject detection model to recognize the subject entity in a question, and a multilevel semantic model to learn the semantic representations for questions and predicates. These models share parameters, and can be trained jointly. We evaluated the proposed method on both English and Chinese KBQA datasets. The experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art systems when applied to both datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
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