Articles

Training higher-order conditional fields is prohibitive for huge tag sets.random We present an approximated conditional random field using coarse-to-fine decoding and early updating. We show that our implementation yields fast and accurate morphological taggers across six languages with different morpho- logical properties and that across languages higher-order models give significant …...

Language models can be formalized as log- linear regression models where the input fea- tures represent previously observed contexts up to a certain length m. The complexity of existing algorithms to learn the parameters by maximum likelihood scale linearly in nd, where n is the length of the training corpus …...

Ngram language models tend to increase in size with inflating the corpus size, and con- sume considerable resources. In this pa- per, we propose an efficient method for im- plementing ngram models based on double- array structures. First, we propose a method for representing backwards suffix trees using double-array structures …...

Beam search is a fast and empirically effective method for translation decoding, but it lacks formal guarantees about search error. We de- velop a new decoding algorithm that combines the speed of beam search with the optimal cer- tificate property of Lagrangian relaxation, and apply it to phrase- and syntax-based …...

Interpreting anaphoric shell nouns (ASNs) such as this issue and this fact is essential to understanding virtually any substantial natural language text. One obstacle in developing methods for automatically interpreting ASNs is the lack of annotated data. We tackle this challenge by exploiting cataphoric shell nouns (CSNs) whose construction makes …...

Coreference resolution metrics quantify errors but do not analyze them. Here, we consider an automated method of categorizing errors in the output of a coreference system into intu- itive underlying error types. Using this tool, we first compare the error distributions across a large set of systems, then analyze common …...

Traditional synchronous grammar induction estimates parameters by maximizing likeli- hood, which only has a loose relation to trans- lation quality. Alternatively, we propose a max-margin estimation approach to discrim- inatively inducing synchronous grammars for machine translation, which directly optimizes translation quality measured by BLEU. In the max-margin estimation of parameters, …...

The creation of a pronunciation lexicon re- mains the most inefficient process in develop- ing an Automatic Speech Recognizer (ASR). In this paper, we propose an unsupervised alternative – requiring no language-specific knowledge – to the conventional manual ap- proach for creating pronunciation dictionar- ies. We present a hierarchical Bayesian …...

In this paper we present a minimally- supervised approach to the multi-domain ac- quisition of wide-coverage glossaries. We start from a small number of hypernymy rela- tion seeds and bootstrap glossaries from the Web for dozens of domains using Probabilis- tic Topic Models. Our experiments show that we are able …...

Domain adaptation has been popularly stud- ied on exploiting labeled information from a source domain to learn a prediction model in a target domain. In this paper, we develop a novel representation learning approach to ad- dress domain adaptation for text classification with automatically induced discriminative la- tent features, which …...

This paper proposes a novel noise-aware char- acter alignment method for bootstrapping sta- tistical machine transliteration from automat- ically extracted phrase pairs. The model is an extension of a Bayesian many-to-many alignment method for distinguishing non- transliteration (noise) parts in phrase pairs. It worked effectively in the experiments of boot- …...

We present a novel vector space model for se- mantic co-compositionality. Inspired by Gen- erative Lexicon Theory (Pustejovsky, 1995), our goal is a compositional model where both predicate and argument are allowed to modify each others’ meaning representations while generating the overall semantics. This readily addresses some major challenges with …...

We introduce a novel method to jointly parse and detect disfluencies in spoken utterances. Our model can use arbitrary features for pars- ing sentences and adapt itself with out-of- domain data. We show that our method, based on transition-based parsing, performs at a high level of accuracy for both the …...

When reviewing scientific literature, it would be useful to have automatic tools that iden- tify the most influential scientific articles as well as how ideas propagate between articles. In this context, this paper introduces topical influence, a quantitative measure of the ex- tent to which an article tends to spread …...

We present a novel model, Freestyle, that learns to improvise rhyming and fluent re- sponses upon being challenged with a line of hip hop lyrics, by combining both bottom- up token based rule induction and top-down rule segmentation strategies to learn a stochas- tic transduction grammar that simultaneously learns both …...

Two recent measures incorporate the notion of statistical significance in basic PMI formula- tion. In some tasks, we find that the new mea- sures perform worse than the PMI. Our anal- ysis shows that while the basic ideas in incor- porating statistical significance in PMI are rea- sonable, they have …...

We seek to measure political candidates’ ideo- logical positioning from their speeches. To ac- complish this, we infer ideological cues from a corpus of political writings annotated with known ideologies. We then represent the speeches of U.S. Presidential candidates as se- quences of cues and lags (filler distinguished only by …...

In this study, we use compositional distribu- tional semantic methods to investigate restric- tions in adjective ordering. Specifically, we focus on properties distinguishing Adjective- Adjective-Noun phrases in which there is flex- ibility in the adjective ordering from those bound to a rigid order. We explore a number of measures extracted …...

In this paper, we address the problem of estimating question difficulty in community question answering services. We propose a competition-based model for estimating ques- tion difficulty by leveraging pairwise compar- isons between questions and users. Our ex- perimental results show that our model sig- nificantly outperforms a PageRank-based ap- proach. …...

Compared to the edited genres that have played a central role in NLP research, mi- croblog texts use a more informal register with nonstandard lexical items, abbreviations, and free orthographic variation. When confronted with such input, conventional text analysis tools often perform poorly. Normalization — replacing orthographically or lexically id- …...