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This paper describes the role of supertagging in a wide-coverage CCG parser which uses a log-linear model to select an analysis. The supertagger reduces the derivation space over which model …...

. Discriminative training methods are used in statistical machine translation to ef- fectively introduce and combine additional knowledge sources within the translation proc- ess. Although these methods are described in …...

We describe a method for discriminative training of a language model that makes use of syntactic features. We follow a reranking approach, where a baseline recogniser is used to produce …...

AbstractDiscriminative training methods have recently led to significant advances in the state of the art of machine translation (MT). Another promising trend is the incorporation of syntactic information into MT …...

Standard approaches to Chinese word seg- mentation treat the problem as a tagging task, assigning labels to the characters in the sequence indicating whether the char- acter marks a word …...

Near-synonyms are useful knowledge re- sources for many natural language applica- tions such as query expansion for information retrieval (IR) and paraphrasing for text gen- eration. However, near-synonyms are not …...

Although discriminative training guarantees to improve statistical machine translation by in- corporating a large amount of overlapping fea- tures, it is hard to scale up to large data due to …...

Traditional text similarity measures consider each term similar only to itself and do not model semantic relatedness of terms. We pro- pose a novel discriminative training method that projects the …...

Discriminative training for machine transla- tion has been well studied in the recent past. A limitation of the work to date is that it relies on the availability of high-quality …...

We introduce a discriminatively trained, glob- ally normalized, log-linear variant of the lex- ical translation models proposed by Brown et al. (1993). In our model, arbitrary, non- independent features may …...

Discriminative training in query spelling cor- rection is difficult due to the complex inter- nal structures of the data. Recent work on query spelling correction suggests a two stage approach …...

Existing vector space models typically map synonyms and antonyms to similar word vec- tors, and thus fail to represent antonymy. We introduce a new vector space representation where antonyms lie …...

Until recently, the application of discrimina- tive training to log linear-based statistical ma- chine translation has been limited to tuning the weights of a limited number of features or training …...

We present the system we developed to provide efficient large-scale feature-rich discriminative training for machine trans- lation. We describe how we integrate with MapReduce using Hadoop streaming to allow arbitrarily …...

This paper presents a general, statistical framework for modeling phrase translation via Markov random fields. The model al- lows for arbituary features extracted from a phrase pair to be incorporated …...

This work presents a flexible and efficient discriminative training approach for statisti- cal machine translation. We propose to use the RPROP algorithm for optimizing a max- imum expected BLEU objective …...

AbstractClassifying the stance expressed in online microblogging social media is an emerging problem in opinion mining. We propose a probabilistic approach to stance classification in tweets, which models stance, target …...

. This paper proposes a new discriminative framework to train continuous-space trans- lation models based. This framework relies on the definition of a new objective function that al- lows us …...

AbstractWe focus on the problem of language modeling for code-switched language, in the context of automatic speech recognition (ASR). Language modeling for code-switched language is challenging for (at least) three …...

AbstractOpen information extraction (OIE) is the task of extracting facts "(Subject, Relation, Object)” from natural language text. We propose several new methods for training neural OIE models in this paper. …...