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Linguistic Theory Based contextual Evidence Mining for Statistical Chinese co-reference resolution

Journal of computer Science & Technology 2007年第4期22卷 608-617页

作者：赵军刘非凡 National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences

Under statistical learning framework, the paper focuses on how to use traditional linguistic findings on anaphora resolution as a guide for mining and organizing contextual features for Chinese co-reference resolution. The main achievements are as follows. （1） In order to simulate ＂syntactic and semantic parallelism factor＂, we extract ＂bags of word form and POS＂ feature and ＂bag of seines＂ feature from the contexts of the entity mentions and incorporate them into the baseline feature set. （2） Because it is too coarse to use the feature of bags of word form, POS tag and seme to determine the syntactic and semantic parallelism between two entity mentions, we propose a method for contextual feature reconstruction based on semantic similarity computation, in order that the reconstructed contextual features could better approximate the anaphora resolution factor of ＂Syntactic and Semantic Parallelism Preferences＂. （3） We use an entity-mention-based contextual feature representation instead of isolated word-based contextual feature representation, and expand the size of the contextual windows in addition, in order to approximately simulate ＂the selectional restriction factor＂ for anaphora resolution. The experiments show that the multi-level contextual features are useful for co-reference resolution, and the statistical system incorporated with these features performs well on the standard ACE datasets.

关键词： natural language processing information extraction co-reference resolution anaphora resolution

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coreference resolution helps visual dialogs to focus

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High-confidence computing 2024年第2期4卷 129-135页

作者： Tianwei Yue Wenping Wang Chen Liang Dachi Chen congrui Hetang Xuewei Wang Carnegie Mellon University Pittsburgh 15213USA

Visual Dialog is a multi-modal task involving both computer vision and dialog *** goal is to answer multiple questions in conversation style,given an image as the *** networks with attention modules are widely used for this task,because of their effectiveness in reasoning the relevance between the texts and *** this work,we study how to further improve the quality of such reasoning,which is an open *** baseline is the Recursive Visual Attention(RVA)model,which refines the vision-text attention by iteratively visiting the dialog *** on top of that,we propose to improve the attention mechanism with contrastive *** train a Matching-Aware Attention Kernel(MAAK)by aligning the deep feature embeddings of an image and its caption,to provide better attention *** show consistent improvements from *** addition,we study the effect of using Multimodal compact Bilinear(MCB)pooling as a three-way feature fusion for the visual,textual and dialog history *** analyze the performance of both methods in the discussion section,and propose further ideas to resolve current limitations.

关键词： Multi-model machine learning Visual dialog co-reference resolution

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