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Representation learning is a promising tech- nique for discovering features that allow su- pervised classifiers to generalize from a source domain dataset to arbitrary new domains. We present a novel, …...

We investigate the task of medical concept coreference resolution in clinical text using two semi-supervised methods, co-training and multi-view learning with posterior regulariza- tion. By extracting semantic and temporal features …...

We present a general framework containing a graded spectrum of Expectation Maximization (EM) algorithms called Unified Expectation Maximization (UEM.) UEM is parameterized by a single parameter and covers existing al- …...

We present a framework for cross-lingual transfer of sequence information from a resource-rich source language to a resource- impoverished target language that incorporates soft constraints via posterior regularization. To this …...

We present a flexible formulation of semi- supervised learning for structured mod- els, which seamlessly incorporates graph- based and more general supervision by ex- tending the posterior regularization (PR) framework. …...

Clustering aspect-related phrases in terms of product’s property is a precursor pro- cess to aspect-level sentiment analysis which is a central task in sentiment analy- sis. Most of existing methods …...

This paper proposes a novel context-aware method for analyzing sentiment at the level of individual sentences. Most ex- isting machine learning approaches suf- fer from limitations in the modeling of …...

This study investigates on building a better Chinese word segmentation mod- el for statistical machine translation. aims at leveraging word boundary infor-It mation, automatically learned by bilin- gual character-based alignments, …...

Generative word alignment models, such as IBM Models, are restricted to one- to-many alignment, and cannot explicitly represent many-to-many relationships in a bilingual text. The problem is par- tially solved …...

Park and Cardie (2014) proposed a novel task of automatically identifying appropriate types of support for propositions comprising online user comments, as an essential step toward au- tomated analysis of …...

We present Model Invertibility Regularization (MIR), a method that jointly trains two direc- tional sequence alignment models, one in each direction, and takes into account the invertibil- ity of the …...

Regulating deep neural networks (DNNs) with human structured knowledge has shown to be of great benefit for improved accuracy and in- terpretability. We develop a general frame- work that enables …...

We propose a novel unsupervised word align- ment method that uses a constraint based on Inversion Transduction Grammar (ITG) parse trees to jointly unify two directional mod- els. Previous agreement …...

AbstractAlthough neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge. In this work, we propose to use posterior regularization …...

AbstractThis paper provides a new way to improve the efficiency of the REINFORCE training process. We apply it to the task of instance selection in distant supervision. Modeling the instance …...

AbstractAdvanced machine learning techniques have boosted the performance of natural language processing. Nevertheless, recent studies, e.g., (CITATION) show that these techniques inadvertently capture the societal bias hidden in the corpus …...

AbstractSentiment analysis is an important task in natural language processing (NLP). Most of existing state-of-the-art methods are under the supervised learning paradigm. However, human annotations can be scarce. Thus, we …...

AbstractAbductive reasoning aims to find plausible explanations for an event. This style of reasoning is critical for commonsense tasks where there are often multiple plausible explanations. Existing approaches for abductive …...