Articles

In this paper we explore the utility of sen- timent analysis and semantic word classes for improving why-question answering on a large-scale web corpus. Our work is moti- vated by the observation that a why-question and its answer often follow the pattern that if something undesirable happens, the reason is …...

We present results of a novel experiment to in- vestigate speech production in conversational data that links speech rate to information den- sity. We provide the first evidence for an asso- ciation between syntactic surprisal and word duration in recorded speech. Using the AMI corpus which contains transcriptions of focus …...

Many linguistic and textual processes involve transduction of strings. We show how to learn a stochastic transducer from an unorganized collection of strings (rather than string pairs). The role of the transducer is to organize the collection. Our generative model explains similarities among the strings by supposing that some strings …...

We consider the problem of inducing grapheme-to-phoneme mappings for un- known languages written in a Latin alphabet. First, we collect a data-set of 107 languages with known grapheme-phoneme relationships, along with a short text in each language. We then cast our task in the framework of super- vised learning, where …...

State-of-the-art graph-based parsers use fea- tures over higher-order dependencies that rely on decoding algorithms that are slow and difficult to generalize. On the other hand, transition-based dependency parsers can eas- ily utilize such features without increasing the linear complexity of the shift-reduce system beyond a constant. In this paper, we …...

The possibility of deleting a word from a sen- tence without violating its syntactic correct- ness belongs to traditionally known manifes- tations of syntactic dependency. We introduce a novel unsupervised parsing approach that is based on a new n-gram reducibility measure. We perform experiments across 18 languages available in CoNLL …...

In this paper, we show that significant im- provements in the accuracy of well-known transition-based parsers can be obtained, with- out sacrificing efficiency, by enriching the parsers with simple transitions that act on buffer nodes. First, we show how adding a specific tran- sition to create either a left or …...

We propose a novel, language-independent approach for improving machine translation from a resource-poor language to X by adapt- ing a large bi-text for a related resource-rich language and X (the same target language). We assume a small bi-text for the resource- poor language to X pair, which we use to …...

Tense is a small element to a sentence, how- ever, error tense can raise odd grammars and result in misunderstanding. Recently, tense has drawn attention in many natural language processing applications. However, most of current Statistical Machine Translation (SMT) systems mainly depend on translation model and language model. They never …...

We apply slice sampling to Bayesian de- cipherment and use our new decipherment framework to improve out-of-domain machine translation. Compared with the state of the art algorithm, our approach is highly scalable and produces better results, which allows us to decipher ciphertext with billions of tokens and hundreds of thousands …...

We present a new approach to the problem of automatic text summarization called Au- tomatic Summarization using Reinforcement Learning (ASRL) in this paper, which models the process of constructing a summary within the framework of reinforcement learning and attempts to optimize the given score function with the given feature representation …...

Comprehension and corpus studies have found that the tendency to minimize dependency length has a strong influence on constituent or- dering choices. In this paper, we investigate dependency length minimization in the con- text of discriminative realization ranking, fo- cusing on its potential to eliminate egregious ordering errors as well …...

Multi-document summarization involves many aspects of content selection and sur- face realization. The summaries must be informative, succinct, grammatical, and obey stylistic writing conventions. We present a method where such individual aspects are learned separately from data (without any hand-engineering) but optimized jointly using an integer linear programme. The ILP …...

We describe a nonparametric model and corresponding inference algorithm for learning Synchronous Context Grammar derivations for parallel text.Free The model employs a Pitman-Yor Process prior which uses a novel base distribution over synchronous grammar rules. Through both synthetic grammar induction and statistical machine translation experiments, we show that our model …...

Topic models traditionally rely on the bag- of-words assumption. In data mining appli- cations, this often results in end-users being presented with inscrutable lists of topical un- igrams, single words inferred as representa- tive of their topics. In this article, we present a hierarchical generative probabilistic model of topical phrases. …...

Recently there has been substantial interest in using spectral methods to learn generative se- quence models like HMMs. Spectral meth- ods are attractive as they provide globally con- sistent estimates of the model parameters and are very fast and scalable, unlike EM meth- ods, which can get stuck in local …...

Learning inference relations between verbs is at the heart of many semantic applications. However, most prior work on learning such rules focused on a rather narrow set of in- formation sources: mainly distributional sim- ilarity, and to a lesser extent manually con- structed verb co-occurrence patterns. In this paper, we …...

Computational approaches to metonymy res- olution have focused almost exclusively on the local context, especially the constraints placed on a potentially metonymic word by its grammatical collocates. We expand such approaches by taking into account the larger context. Our algorithm is tested on the data from the metonymy resolution task …...

Generating coherent discourse is an important aspect in natural language generation. Our aim is to learn factors that constitute coherent discourse from data, with a focus on how to re- alize predicate-argument structures in a model that exceeds the sentence level. We present an important subtask for this overall goal, …...

This paper focuses on the task of colloca- tion polarity disambiguation. The collocation refers to a binary tuple of a polarity word and a target (such as ⟨long, battery life⟩ or ⟨long, startup⟩), in which the sentiment orientation of the polarity word (“long”) changes along with different targets (“battery life” …...