We address two key challenges in end-to-end event coreference resolution research: (1) the error propagation problem, where an event coreference resolver has to assume as input the noisy outputs produced by its upstream components in the standard information extraction (IE) pipeline; and (2) the data annotation bottleneck, where manually annotating data for all the components in the IE pipeline is prohibitively expensive. This is the case in the vast majority of the world's natural languages, where such annotated resources are not readily available. To address these problems, we propose to perform joint inference over a lightly supervised IE pipeline, where all the models are trained using either active learning or unsupervised learning. Using our approach, only 25% of the training sentences in the Chinese portion of the ACE 2005 corpus need to be annotated with entity and event mentions in order for our event coreference resolver to surpass its fully supervised counterpart in performance.

While unsupervised anaphoric zero pronoun (AZP) resolvers have recently been shown to rival their supervised counterparts in performance, it is relatively difficult to scale them up to reach the next level of performance due to the large amount of feature engineering efforts involved and their ineffectiveness in exploiting lexical features.To address these weaknesses, we propose a supervised approach to AZP resolution based on deep neural networks, taking advantage of their ability to learn useful task-specific representations and effectively exploit lexical features via word embeddings.Our approach achieves stateof-the-art performance when resolving the Chinese AZPs in the OntoNotes corpus.

Pronoun resolution and common noun phrase resolution are the two most challenging subtasks of coreference resolution. While a lot of work has focused on pronoun resolution, common noun phrase resolution has almost always been tackled in the context of the larger coreference resolution task. In fact, to our knowledge, there has been no attempt to address Chinese common noun phrase resolution as a standalone task. In this paper, we propose a generative model for unsupervised Chinese common noun phrase resolution that not only allows easy incorporation of linguistic constraints on coreference but also performs joint resolution and anaphoricity determination. When evaluated on the Chinese portion of the OntoNotes 5.0 corpus, our model rivals its supervised counterpart in performance.

Recent work has successfully leveraged the semantic information extracted from lexical knowledge bases such as WordNet and FrameNet to improve English event coreference resolvers.The lack of comparable resources in other languages, however, has made the design of high-performance non-English event coreference resolvers, particularly those employing unsupervised models, very difficult.We propose a generative model for the under-studied task of Chinese event coreference resolution that rivals its supervised counterparts in performance when evaluated on the ACE 2005 corpus.

Much research has been done on the problem of English pronoun resolution, but there has been relatively little work on the corresponding problem of Chinese pronoun resolution. While pronoun resolution in both languages remains a challenging task, Chinese pronoun resolution is further complicated by (1) the lack of publicly available Chinese word lists or dictionaries that can be used to look up essential mention attributes such as gender and number; and (2) the relative dearth of Chinese coreference-annotated data. Existing approaches to Chinese pronoun resolution are monolingual, training and testing a pronoun resolver on Chinese data. In contrast, we propose a bilingual approach to Chinese pronoun resolution, aiming to improve the resolution of Chinese pronouns by leveraging the publicly available English dictionaries and coreference annotations. Experiments on the OntoNotes 5.0 corpus demonstrate that our bilingual approach to Chinese pronoun resolution significantly surpasses the performance of state-of-the-art monolingual approaches.

State-of-the-art approaches to Chinese zero pronoun resolution are supervised, requiring training documents with manually resolved zero pronouns. To eliminate the reliance on annotated data, we propose an unsupervised approach to this task. Underlying our approach is the novel idea of employing a model trained on manually resolved overt pronouns to resolve zero pronouns. Experimental results on the OntoNotes 5.0 corpus are encouraging: our unsupervised model surpasses its supervised counterparts in performance.

State-of-the-art Chinese zero pronoun res-olution systems are supervised, thus re-lying on training data containing manu-ally resolved zero pronouns. To elimi-nate the reliance on annotated data, we present a generative model for unsuper-vised Chinese zero pronoun resolution. At the core of our model is a novel hy-pothesis: a probabilistic pronoun resolver trained on overt pronouns in an unsuper-vised manner can be used to resolve zero pronouns. Experiments demonstrate that our unsupervised model rivals its state-of-the-art supervised counterparts in perfor-mance when resolving the Chinese zero pronouns in the OntoNotes corpus. 1

Several state-of-the-art event extraction systems employ models based on Support Vector Machines (SVMs) in a pipeline architecture, which fails to exploit the joint dependencies that typically exist among events and arguments.While there have been attempts to overcome this limitation using Markov Logic Networks (MLNs), it remains challenging to perform joint inference in MLNs when the model encodes many high-dimensional sophisticated features such as those essential for event extraction.In this paper, we propose a new model for event extraction that combines the power of MLNs and SVMs, dwarfing their limitations.The key idea is to reliably learn and process high-dimensional features using SVMs; encode the output of SVMs as low-dimensional, soft formulas in MLNs; and use the superior joint inferencing power of MLNs to enforce joint consistency constraints over the soft formulas.We evaluate our approach for the task of extracting biomedical events on the BioNLP 2013BioNLP , 2011BioNLP and 2009 Genia shared task datasets.Our approach yields the best F1 score to date on the BioNLP'13 (53.61) and BioNLP'11 (58.07) datasets and the second-best F1 score to date on the BioNLP'09 dataset (58.16).

We extend Zhao and Ng's (2007) Chinese anaphoric zero pronoun resolver by (1) using a richer set of features and (2) exploiting the coreference links between zero pronouns during resolution.Results on OntoNotes show that our approach significantly outperforms two state-of-the-art anaphoric zero pronoun resolvers.To our knowledge, this is the first work to report results obtained by an end-toend Chinese zero pronoun resolver.