Articles

Almost all Chinese language processing tasks involve word segmentation of the language input as their first steps, thus robust and reli- able segmentation techniques are always re- quired to make sure those tasks well- performed. In recent years, machine learning and sequence labeling models such as Condi- tional Random Fields …...

We present a novel approach to distributional- only, fully unsupervised, POS tagging, based on an adaptation of the EM algorithm for the esti- mation of a Gaussian mixture. In this approach, which we call Latent-Descriptor Clustering (LDC), word types are clustered using a series of progressively more informative descriptor vectors. …...

Using multi-layer neural networks to esti- mate the probabilities of word sequences is a promising research area in statistical lan- guage modeling, with applications in speech recognition and statistical machine transla- tion. However, training such models for large vocabulary tasks is computationally challeng- ing which does not scale easily to …...

Problems stemming from domain adaptation continue to plague the statistical natural lan- guage processing community. There has been continuing work trying to find general purpose algorithms to alleviate this problem. In this paper we argue that existing general purpose approaches usually only focus on one of two issues related to …...

While a significant amount of research has been devoted to textual entailment, automated entailment from conversational scripts has re- ceived less attention. To address this limi- tation, this paper investigates the problem of conversation entailment: automated inference of hypotheses from conversation scripts. We examine two levels of semantic representa- tions: …...

Graph-based methods have gained attention in many areas of Natural Language Processing (NLP) including Word Sense Disambiguation (WSD), text summarization, keyword extrac- tion and others. Most of the work in these ar- eas formulate their problem in a graph-based setting and apply unsupervised graph cluster- ing to obtain a set …...

This paper examines tagging models for spon- taneous English speech transcripts. We ana- lyze the performance of state-of-the-art tag- ging models, either generative or discrimi- native, left-to-right or bidirectional, with or without latent annotations, together with the use of ToBI break indexes and several meth- ods for segmenting the speech …...

We define a probabilistic morphological ana- lyzer using a data-driven approach for Syriac in order to facilitate the creation of an annotated corpus. Syriac is an under-resourced Semitic language for which there are no available lan- guage tools such as morphological analyzers. We introduce novel probabilistic models for segmentation, dictionary …...

In many NLP systems, there is a unidirectional flow of information in which a parser supplies input to a semantic role labeler. In this paper, we build a sys- tem that allows information to flow in both direc- tions. We make use of semantic role predictions in choosing a single-best …...

It is well known that parsing accuracies drop significantly on out-of-domain data. What is less known is that some parsers suffer more from domain shifts than others. We show that dependency parsers have more difficulty parsing questions than constituency parsers. In particular, deterministic shift-reduce depen- dency parsers, which are of …...

This paper approaches the scope learning problem via simplified shallow semantic pars- ing. This is done by regarding the cue as the predicate and mapping its scope into several constituents as the arguments of the cue. Evaluation on the BioScope corpus shows that the structural information plays a critical role …...

Parser disambiguation with precision gram- mars generally takes place via statistical rank- ing of the parse yield of the grammar using a supervised parse selection model. In the standard process, the parse selection model is trained over a hand-disambiguated treebank, meaning that without a significant investment of effort to produce …...

We present a new syntactic parser that works left-to-right and top down, thus maintaining a fully-connected parse tree for a few alternative parse hypotheses. All of the commonly used statistical parsers use context-free dynamic programming al- gorithms and as such work bottom up on the entire sentence. Thus they only …...

We introduce a novel training algorithm for unsupervised grammar induction, called Zoomed Learning. Given a training set T and a test set S, the goal of our algorithm is to identify subset pairs Ti, Si of T and S such that when the unsupervised parser is trained on a training …...

Word alignment plays a central role in statisti- cal MT (SMT) since almost all SMT systems extract translation rules from word aligned parallel training data. While most SMT systems use unsupervised algorithms (e.g. GIZA++) for training word alignment, super- vised methods, which exploit a small amount of human-aligned data, have …...

In this article, an original view on how to improve phrase translation estimates is pro- posed. This proposal is grounded on two main ideas: first, that appropriate examples of a given phrase should participate more in build- ing its translation distribution; second, that paraphrases can be used to better estimate …...

Conventional wisdom dictates that syn- chronous context-free grammars (SCFGs) must be converted to Chomsky Normal Form (CNF) to ensure cubic time decoding. For ar- bitrary SCFGs, this is typically accomplished via the synchronous binarization technique of (Zhang et al., 2006). A drawback to this ap- proach is that it inflates …...

We examine effects that empty categories have on machine translation. Empty categories are elements in parse trees that lack corresponding overt surface forms (words) such as dropped pronouns and markers for control construc- tions. We start by training machine trans- lation systems with manually inserted empty elements. We find that …...

Production of parallel training corpora for the development of statistical machine translation (SMT) systems for resource-poor languages usually requires extensive manual effort. Ac- tive sample selection aims to reduce the la- bor, time, and expense incurred in produc- ing such resources, attaining a given perfor- mance benchmark with the smallest …...

In modern machine translation practice, a sta- tistical phrasal or hierarchical translation sys- tem usually relies on a huge set of trans- lation rules extracted from bi-lingual train- ing data. This approach not only results in space and efficiency issues, but also suffers from the sparse data problem. In this …...