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Doo Soon Kim

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3 papers

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AAAI Conference 2022 Conference Paper

CAISE: Conversational Agent for Image Search and Editing

Hyounghun Kim
Doo Soon Kim
Seunghyun Yoon
Franck Dernoncourt
Trung Bui
Mohit Bansal

Demand for image editing has been increasing as users’ desire for expression is also increasing. However, for most users, image editing tools are not easy to use since the tools require certain expertise in photo effects and have complex interfaces. Hence, users might need someone to help edit their images, but having a personal dedicated human assistant for every user is impossible to scale. For that reason, an automated assistant system for image editing is desirable. Additionally, users want more image sources for diverse image editing works, and integrating an image search functionality into the editing tool is a potential remedy for this demand. Thus, we propose a dataset of an automated Conversational Agent for Image Search and Editing (CAISE). To our knowledge, this is the first dataset that provides conversational image search and editing annotations, where the agent holds a grounded conversation with users and helps them to search and edit images according to their requests. To build such a system, we first collect image search and editing conversations between pairs of annotators. The assistant-annotators are equipped with a customized image search and editing tool to address the requests from the user-annotators. The functions that the assistant-annotators conduct with the tool are recorded as executable commands, allowing the trained system to be useful for real-world application execution. We also introduce a generator-extractor baseline model for this task, which can adaptively select the source of the next token (i. e. , from the vocabulary or from textual/visual contexts) for the executable command. This serves as a strong starting point while still leaving a large human-machine performance gap for useful future work. Data and code are available: https: //github. com/hyounghk/CAISE.

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AAAI Conference 2016 Conference Paper

Improving Opinion Aspect Extraction Using Semantic Similarity and Aspect Associations

Qian Liu
Bing Liu
Yuanlin Zhang
Doo Soon Kim
Zhiqiang Gao

Aspect extraction is a key task of ﬁne-grained opinion mining. Although it has been studied by many researchers, it remains to be highly challenging. This paper proposes a novel unsupervised approach to make a major improvement. The approach is based on the framework of lifelong learning and is implemented with two forms of recommendations that are based on semantic similarity and aspect associations respectively. Experimental results using eight review datasets show the effectiveness of the proposed approach.

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AAAI Conference 2013 Conference Paper

Joint Extraction and Labeling via Graph Propagation for Dictionary Construction

Doo Soon Kim
Kunal Verma
Peter Yeh

In this paper, we present an approach that jointly infers the boundaries of tokens and their labels to construct dictionaries for Information Extraction. Our approach for joint-inference is based on graph propagation, and extends it in two novel ways. First, we extend the graph representation to capture ambiguities that occur during the token extraction phase. Second, we modify the labeling phase (i. e. , label propagation) to utilize this new representation, allowing evidence from labeling to be used for token extraction. Our evaluation shows these extensions (and hence our approach) significantly improve the performance of the outcome dictionaries over pipeline-based approaches by preventing aggressive commitment. Our evaluation also shows that our extensions over a base graph-propagation framework improve the precision without hurting the recall.

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