Articles

Toponym resolvers identify the specific lo- cations referred to by ambiguous place- names in text. Most resolvers are based on heuristics using spatial relationships be- tween multiple toponyms in a document, or metadata such as population. This pa- per shows that text-driven disambiguation for toponyms is far more effective. We …...

In this paper we examine language mod- eling for text simplification. Unlike some text-to-text translation tasks, text simplifi- cation is a monolingual translation task al- lowing for text in both the input and out- put domain to be used for training the lan- guage model. We explore the relation- ship …...

We propose a joint inference algorithm for grammatical error correction. Different from most previous work where different error types are corrected independently, our proposed inference process considers all possible errors in a uni�ed framework. We use integer linear programming (ILP) to model the inference process, which can easily incorporate both …...

Word-final /t/-deletion refers to a common phenomenon in spoken English where words such as /wEst/ “west” are pro- nounced as [wEs] “wes” in certain con- texts. Phonological variation like this is common in naturally occurring speech. Current computational models of unsu- pervised word segmentation usually as- sume idealized input that …...

We present BRAINSUP, an extensible framework for the generation of creative sentences in which users are able to force several words to appear in the sen- tences and to control the generation pro- cess across several semantic dimensions, namely emotions, colors, domain related- ness and phonetic properties. We evalu- ate …...

Words often gain new senses in new do- mains. Being able to automatically iden- tify, from a corpus of monolingual text, which word tokens are being used in a pre- viously unseen sense has applications to machine translation and other tasks sensi- tive to lexical semantics. We define a task, …...

Currently, almost all of the statistical ma- chine translation (SMT) models are trained with the parallel corpora in some specific domains. However, when it comes to a lan- guage pair or a different domain without any bilingual resources, the traditional SMT loses its power. Recently, some research works study the …...

We consider the problem of using sentence compression techniques to facilitate query- focused multi-document summarization. We present a sentence-compression-based frame- work for the task, and design a series of learning-based compression models built on parse trees. An innovative beam search de- coder is proposed to efficiently find highly probable compressions. …...

Parallel text is the fuel that drives modern machine translation systems. The Web is a comprehensive source of preexisting par- allel text, but crawling the entire web is impossible for all but the largest compa- nies. We bring web-scale parallel text to the masses by mining the Common Crawl, a …...

We present a new bilingual FrameNet lex- icon for English and German. It is cre- ated through a simple, but powerful ap- proach to construct a FrameNet in any language using Wiktionary as an inter- lingual representation. Our approach is based on a sense alignment of FrameNet and Wiktionary, and …...

We create an open multilingual wordnet with large wordnets for over 26 languages and smaller ones for 57 languages. It is made by combining wordnets with open li- cences, data from Wiktionary and the Uni- code Common Locale Data Repository. Overall there are over 2 million senses for over 100 …...

Semantic similarity is an essential com- ponent of many Natural Language Pro- cessing applications. However, prior meth- ods for computing semantic similarity of- ten operate at different levels, e.g., sin- gle words or entire documents, which re- quires adapting the method for each data type. We present a unified approach …...

We present a hybrid natural language gen- eration (NLG) system that consolidates macro and micro planning and surface re- alization tasks into one statistical learn- ing process. Our novel approach is based on deriving a template bank automatically from a corpus of texts from a target do- main. First, we …...

Generation for Focused Meeting Summarization Lu Wang Department of Computer Science Cornell University Ithaca, NY 14853 luwang@cs.cornell.edu Claire Cardie Department of Computer Science Cornell University Ithaca, NY 14853 cardie@cs.cornell.edu Abstract We address the challenge of generating natu- ral language abstractive summaries for spoken meetings in a domain-independent fashion. We apply …...

Automatic acquisition of inference rules for predicates has been commonly ad- dressed by computing distributional simi- larity between vectors of argument words, operating at the word space level. A re- cent line of work, which addresses context sensitivity of rules, represented contexts in a latent topic space and computed similar- …...

Hierarchical Bayesian Models (HBMs) have been used with some success to capture empirically observed pat- terns of under- and overgeneralization in child language acquisition. How- ever, as is well known, HBMs are “ideal” learning systems, assuming ac- cess to unlimited computational re- sources that may not be available to child …...

We study the task of entity linking for tweets, which tries to associate each mention in a tweet with a knowledge base entry. Two main challenges of this task are the dearth of information in a single tweet and the rich entity mention variations. aTo address these challenges, we propose …...

Speaker identification is the task of at- tributing utterances to characters in a lit- erary narrative. It is challenging to auto- mate because the speakers of the majority of utterances are not explicitly identified in novels. In this paper, we present a super- vised machine learning approach for the task …...

We present a method for automatically generating input parsers from English specifications of input file formats. We use a Bayesian generative model to cap- ture relevant natural language phenomena and translate the English specification into a specification tree, which is then trans- lated into a C++ input parser. We model …...

This paper proposes a new approach to domain adaptation in statistical machine translation (SMT) based on a vector space model (VSM). The general idea is first to create a vector profile for the in-domain development (“dev”) set. This profile might, for instance, be a vector with a di- mensionality equal …...