Articles

Incorporating semantic information in the Statistical Machine Translation (SMT) framework is starting to gain some populari- ty in both the semantics and translation communities. In this paper, we present en- couraging results obtained from experi- ments conducted on English to Arabic SMT system using static, dynamic, and hybrid in- tegration …...

In this paper we investigate the applica- tion of uncertainty detection to text wa- termarking, a problem where the aim is to produce individually identifiable copies of a source text via small manipulations to the text (e.g. synonym substitutions). As previous attempts showed, accurate para- phrasing is challenging in an …...

In this paper, we propose a selective com- bination approach of pivot and direct sta- tistical machine translation (SMT) models to improve translation quality. We work with Persian-Arabic SMT as a case study. We show positive results (from 0.4 to 3.1 BLEU on different direct training corpus sizes) in addition …...

Previous research on quality estimation for machine translation has demonstrated the possibility of predicting the transla- tion quality of well-formed data. We present a first study on estimating the translation quality of user-generated con- tent. Our dataset contains English tech- nical forum comments which were trans- lated into French by …...

We propose a simple and effective method to build a meta-level Statistical Machine Transla- tion (SMT), called meta-SMT, for system combination. Our approach is based on the framework of Stacked Generalization, also known as Stacking, which is an ensemble learning algorithm, widely used in machine learning tasks. First, a collection …...

Although parallel sentences rarely exist in quasi–comparable corpora, there could be parallel fragments that are also helpful for statistical machine translation (SMT). Previous studies cannot accurately extract parallel fragments from quasi–comparable corpora. To solve this problem, we pro- pose an accurate parallel fragment extrac- tion system that uses an alignment …...

Recognizing Chinese Named entities from the Web is challenging, due to the lack of labeled data and differences between Chi- nese and English. We propose a semi- supervised approach which leverages seed entities and the large unlabeled data to learn templates. Some high-quality tem- plates are generated iteratively to extrac- …...

We present a novel approach to learning phrasal inversion transduction grammars via Bayesian MAP (maximum a posteri- ori) or information-theoretic MDL (mini- mum description length) model optimiza- tion so as to incorporate simultaneously the choices of model structure as well as pa- rameters. In comparison to most current SMT approaches, …...

Existing methods for collecting texts from endangered languages are not creating the quantity of data that is needed for cor- pus studies and natural language process- ing tasks. This is because the process of transcribing and translating from audio recordings is too onerous. A more effec- tive method, we argue, …...

We describe a first experiment on the identification and extraction of computer- interpretable guideline (CIG) components (activities, actors and consumed artifacts) from clinical documents, based on clinical entity recognition techniques. We rely on MetaMap and the UMLS Metathesaurus to provide lexical information, and study the impact of clinical document syntax …...

We propose a novel approach to abstrac- tive Web summarization based on the ob- servation that summaries for similar URLs tend to be similar in both content and structure. We leverage existing URL clus- ters and construct per-cluster word graphs that combine known summaries while ab- stracting out URL-specific attributes. …...

In this paper, we propose a semantic naïve Bayes classifier (SNBC) to improve the conventional naïve Bayes classifier (NBC) by incorporating “document-level” semantic information for document classification (DC). To capture the semantic information from each document, we develop semantic feature extraction and modeling algorithms. For semantic feature extraction, we first …...

The paper addresses the problem of text variation which often hinders interopera- ble use or reuse of corpora and annota- tions. A systematic solution is presented based on a variation of Longest Common Sequence algorithm. An empirical exper- iment with 20 full text articles shows it works well with a …...

Classical approaches to sentiment classi- fication exploit only textual features in a given review and are not aware of the per- sonality of the user or the public sentiment toward the target product. In this paper, we propose a model that can accurately es- timate the sentiment polarity by referring …...

We examine several quantitative tech- niques of authorship attribution that have gained importance over the time and com- pare them with the current state of the art Z-score based technique. In this paper we show how comparable the existing tech- niques can be to the Z-score based method, simply by …...

We report on experiments designed to in- vestigate the role of syntactic features in the task of quality estimation for machine translation, focusing on the effect of parser accuracy. Tree kernels are used to predict the segment-level BLEU score of English- French translations. In order to examine the effect of …...

This paper presents a joint model for learning morphology and part-of-speech (PoS) tags simultaneously. The proposed method adopts a finite mixture model that groups words having similar contextual features thereby assigning the same PoS tag to those words. While learning PoS tags, words are analysed morphologically by exploiting similar morphological …...

Systems processing on natural language text encounters fatal problems due to long and complex sentences. Their perfor- mance degrades as the complexity of the sentence increases. This paper addresses the task of simplifying complex sentences in Hindi into multiple simple sentences, using a rule based approach. Our approach utilizes two …...

Various successful methods for synonym acquisition are based on comparing con- text vectors acquired from a monolingual corpus. However, a domain-specific cor- pus might be limited in size and, as a con- sequence, a query term’s context vector can be sparse. Furthermore, even terms in a domain-specific corpus are sometimes …...

We introduce an online framework for dis- criminative learning problems over hidden structures, where we learn both the latent structure and the classifier for a supervised learning task. Previous work on lever- aging latent representations for discrimi- native learners has used batch algorithms that require multiple passes though the en- …...