Articles

In this paper, we present an algorithm for learning a generative model of natural lan- guage sentences together with their for- mal meaning representations with hierarchi- cal structures. The model is applied to the task of mapping sentences to hierarchical rep- resentations of their underlying meaning. We introduce dynamic programming …...

Chinese is a language that does not have mor- phological tense markers that provide explicit grammaticalization of the temporal location of situations (events or states). However, in many NLP applications such as Machine Transla- tion, Information Extraction and Question An- swering, it is desirable to make the temporal location of …...

Previous work on ordering events in text has typically focused on local pairwise decisions, ignoring globally inconsistent labels. How- ever, temporal ordering is the type of domain in which global constraints should be rela- tively easy to represent and reason over. This paper presents a framework that informs lo- cal …...

NLP tasks are often domain specific, yet sys- tems can learn behaviors across multiple do- mains. We develop a new multi-domain online learning framework based on parameter com- bination from multiple classifiers. Our algo- rithms draw from multi-task learning and do- main adaptation to adapt multiple source do- main classifiers …...

We present an algorithmic framework for learning multiple related tasks. Our frame- work exploits a form of prior knowledge that relates the output spaces of these tasks. We present PAC learning results that analyze the conditions under which such learning is pos- sible. We present results on learning a shal- …...

We present a novel learning framework for pipeline models aimed at improving the com- munication between consecutive stages in a pipeline. Our method exploits the confidence scores associated with outputs at any given stage in a pipeline in order to compute prob- abilistic features used at other stages down- stream. …...

This paper investigates two strategies for im- proving coreference resolution: (1) training separate models that specialize in particu- lar types of mentions (e.g., pronouns versus proper nouns) and (2) using a ranking loss function rather than a classification function. In addition to being conceptually simple, these modifications of the standard …...

Machine learning approaches to coreference resolution are typically supervised, and re- quire expensive labeled data. Some unsuper- vised approaches have been proposed (e.g., Haghighi and Klein (2007)), but they are less accurate. In this paper, we present the first un- supervised approach that is competitive with supervised ones. This is …...

We present Minimum Bayes-Risk (MBR) de- coding over translation lattices that compactly encode a huge number of translation hypothe- ses. We describe conditions on the loss func- tion that will enable efficient implementation of MBR decoders on lattices. We introduce an approximation to the BLEU score (Pap- ineni et al., …...

B is the de facto standard for evaluation and development of statistical machine trans- lation systems. We describe three real-world situations involving comparisons between dif- ferent versions of the same systems where one can obtain improvements in B scores that are questionable or even absurd. These situ- ations arise because …...

Relationship discovery is the task of iden- tifying salient relationships between named entities in text. We propose novel approaches for two sub-tasks of the problem: identifying the entities of interest, and partitioning and describing the relations based on their semantics. In particular, we show that term frequency patterns can be …...

While significant effort has been put into an- notating linguistic resources for several lan- guages, there are still many left that have only small amounts of such resources. This paper investigates a method of propagat- ing information (specifically mention detec- tion information) into such low resource languages from richer ones. …...

We present a graph-based semi-supervised la- bel propagation algorithm for acquiring open- domain labeled classes and their instances from a combination of unstructured and struc- tured text sources. This acquisition method significantly improves coverage compared to a previous set of labeled classes and instances derived from free text, while achieving …...

Syntactic word reordering is essential for translations across different grammar struc- tures between syntactically distant language- pairs. In this paper, we propose to em- bed local and non-local word reordering de- cisions in a synchronous context free gram- mar, and leverages the grammar in a chart- based decoder. Local word-reordering …...

We present a generative model for unsuper- vised coreference resolution that views coref- erence as an EM clustering process. For comparison purposes, we revisit Haghighi and Klein’s (2007) fully-generative Bayesian model for unsupervised coreference resolu- tion, discuss its potential weaknesses and con- sequently propose three modifications to their model. Experimental …...

Graph-based and transition-based approaches to dependency parsing adopt very different views of the problem, each view having its own strengths and limitations. We study both approaches under the framework of beam- search. By developing a graph-based and a transition-based dependency parser, we show that a beam-search decoder is a competitive …...

It is a challenging task to identify sentiment polarity of Chinese reviews because the re- sources for Chinese sentiment analysis are limited. Instead of leveraging only monolin- gual Chinese knowledge, this study proposes a novel approach to leverage reliable English resources to improve Chinese sentiment analysis. Rather than simply projecting …...

We examine the problem of content selection in statistical novel sentence generation. Our approach models the processes performed by professional editors when incorporating ma- terial from additional sentences to support some initially chosen key summary sentence, a process we refer to as Sentence Augmen- tation. We propose and evaluate a …...

Obtaining labeled data is a significant obstacle for many NLP tasks. Recently, online games have been proposed as a new way of obtain- ing labeled data; games attract users by be- ing fun to play. In this paper, we consider the application of this idea to collecting seman- tic relations …...

This paper describes a new automatic method for Japanese predicate argument structure analysis. The method learns relevant features to assign case roles to the argument of the tar- get predicate using the features of the words located closest to the target predicate under various constraints such as dependency types, words, …...