Hong Li


2020

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Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements
Yang Li | Gang Li | Luheng He | Jingjie Zheng | Hong Li | Zhiwei Guan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Natural language descriptions of user interface (UI) elements such as alternative text are crucial for accessibility and language-based interaction in general. Yet, these descriptions are constantly missing in mobile UIs. We propose widget captioning, a novel task for automatically generating language descriptions for UI elements from multimodal input including both the image and the structural representations of user interfaces. We collected a large-scale dataset for widget captioning with crowdsourcing. Our dataset contains 162,860 language phrases created by human workers for annotating 61,285 UI elements across 21,750 unique UI screens. We thoroughly analyze the dataset, and train and evaluate a set of deep model configurations to investigate how each feature modality as well as the choice of learning strategies impact the quality of predicted captions. The task formulation and the dataset as well as our benchmark models contribute a solid basis for this novel multimodal captioning task that connects language and user interfaces.

2015

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A Web-based Collaborative Evaluation Tool for Automatically Learned Relation Extraction Patterns
Leonhard Hennig | Hong Li | Sebastian Krause | Feiyu Xu | Hans Uszkoreit
Proceedings of ACL-IJCNLP 2015 System Demonstrations

2014

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Annotating Relation Mentions in Tabloid Press
Hong Li | Sebastian Krause | Feiyu Xu | Hans Uszkoreit | Robert Hummel | Veselina Mironova
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents a new resource for the training and evaluation needed by relation extraction experiments. The corpus consists of annotations of mentions for three semantic relations: marriage, parent―child, siblings, selected from the domain of biographic facts about persons and their social relationships. The corpus contains more than one hundred news articles from Tabloid Press. In the current corpus, we only consider the relation mentions occurring in the individual sentences. We provide multi-level annotations which specify the marked facts from relation, argument, entity, down to the token level, thus allowing for detailed analysis of linguistic phenomena and their interactions. A generic markup tool Recon developed at the DFKI LT lab has been utilised for the annotation task. The corpus has been annotated by two human experts, supported by additional conflict resolution conducted by a third expert. As shown in the evaluation, the annotation is of high quality as proved by the stated inter-annotator agreements both on sentence level and on relationmention level. The current corpus is already in active use in our research for evaluation of the relation extraction performance of our automatically learned extraction patterns.

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Language Resources and Annotation Tools for Cross-Sentence Relation Extraction
Sebastian Krause | Hong Li | Feiyu Xu | Hans Uszkoreit | Robert Hummel | Luise Spielhagen
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we present a novel combination of two types of language resources dedicated to the detection of relevant relations (RE) such as events or facts across sentence boundaries. One of the two resources is the sar-graph, which aggregates for each target relation ten thousands of linguistic patterns of semantically associated relations that signal instances of the target relation (Uszkoreit and Xu, 2013). These have been learned from the Web by intra-sentence pattern extraction (Krause et al., 2012) and after semantic filtering and enriching have been automatically combined into a single graph. The other resource is cockrACE, a specially annotated corpus for the training and evaluation of cross-sentence RE. By employing our powerful annotation tool Recon, annotators mark selected entities and relations (including events), coreference relations among these entities and events, and also terms that are semantically related to the relevant relations and events. This paper describes how the two resources are created and how they complement each other.

2012

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Annotating Opinions in German Political News
Hong Li | Xiwen Cheng | Kristina Adson | Tal Kirshboim | Feiyu Xu
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper presents an approach to construction of an annotated corpus for German political news for the opinion mining task. The annotated corpus has been applied to learn relation extraction rules for extraction of opinion holders, opinion content and classification of polarities. An adapted annotated schema has been developed on top of the state-of-the-art research. Furthermore, a general tool for annotating relations has been utilized for the annotation task. An evaluation of the inter-annotator agreement has been conducted. The rule learning is realized with the help of a minimally supervised machine learning framework DARE.

2011

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Minimally Supervised Domain-Adaptive Parse Reranking for Relation Extraction
Feiyu Xu | Hong Li | Yi Zhang | Hans Uszkoreit | Sebastian Krause
Proceedings of the 12th International Conference on Parsing Technologies

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Minimally Supervised Rule Learning for the Extraction of Biographic Information from Various Social Domains
Hong Li | Feiyu Xu | Hans Uszkoreit
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

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META-DARE: Monitoring the Minimally Supervised ML of Relation Extraction Rules
Hong Li | Feiyu Xu | Hans Uszkoreit
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

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TechWatchTool: Innovation and Trend Monitoring
Hong Li | Feiyu Xu | Hans Uszkoreit
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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Boosting Relation Extraction with Limited Closed-World Knowledge
Feiyu Xu | Hans Uszkoreit | Sebastian Krause | Hong Li
Coling 2010: Posters

2009

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Gossip Galore – A Self-Learning Agent for Exchanging Pop Trivia
Xiwen Cheng | Peter Adolphs | Feiyu Xu | Hans Uszkoreit | Hong Li
Proceedings of the Demonstrations Session at EACL 2009

2008

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Adaptation of Relation Extraction Rules to New Domains
Feiyu Xu | Hans Uszkoreit | Hong Li | Niko Felger
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper presents various strategies for improving the extraction performance of less prominent relations with the help of the rules learned for similar relations, for which large volumes of data are available that exhibit suitable data properties. The rules are learned via a minimally supervised machine learning system for relation extraction called DARE. Starting from semantic seeds, DARE extracts linguistic grammar rules associated with semantic roles from parsed news texts. The performance analysis with respect to different experiment domains shows that the data property plays an important role for DARE. Especially the redundancy of the data and the connectivity of instances and pattern rules have a strong influence on recall. However, most real-world data sets do not possess the desirable small-world property. Therefore, we propose three scenarios to overcome the data property problem of some domains by exploiting a similar domain with better data properties. The first two strategies stay with the same corpus but try to extract new similar relations with learned rules. The third strategy adapts the learned rules to a new corpus. All three strategies show that frequently mentioned relations can help in the detection of less frequent relations.

2007

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A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity
Feiyu Xu | Hans Uszkoreit | Hong Li
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics