In this study, we examined the pronunciation characteristics of multiword expressions (MWEs). We first drew up an inventory of frequently occurring N-grams extracted from orthographic transcriptions of spontaneous speech contained in a large corpus of spoken Dutch. For about 10% of these Ngrams phonetic transcriptions were available, which were examined. Our results show that the pronunciation ...

This work presents a generalized approach for the fast structural alignment of thousands of macromolecular structures. The method uses string representations of a macromolecular structure and a hash table that stores n-grams of a certain size for searching. To this end, macromolecular structure-to-string translators were implemented for protein and RNA structures. A query against the index is p...

Abstract Despite the modern boom in technology, we are still faced with fact that people write texts without diacritics. There two main reasons for this. The first, historical reason stems from past when use of diacritics was troublesome and would text them. second one is speed - typing usually faster. Text easy to understand people, but some types documents, missing can cause a problem. This a...

We present three novel methods of compactly storing very large n-gram language models. These methods use substantially less space than all known approaches and allow n-gram probabilities or counts to be retrieved in constant time, at speeds comparable to modern language modeling toolkits. Our basic approach generates an explicit minimal perfect hash function, that maps all n-grams in a model to...

We describe and evaluate three systems for automatically predicting the ICD-9-CM codes of radiology reports from short excerpts of text. The first system benefits from an open source search engine, Lucene, and takes advantage of the relevance of reports to one another based on individual words. The second uses BoosTexter, a boosting algorithm based on n-grams (sequences of consecutive words) an...

Statistical language models have found many applications in information retrieval since their introduction almost three decades ago. Currently the most popular models are n-gram models, which are known to suffer from serious sparseness issues, which is a result of the large vocabulary size |V | of any given corpus and of the exponential nature of n-grams, where potentially |V | n-grams can occu...

There is an increasing amount of user-generated information in online documents, including user opinions on various topics and products such as movies, DVDs, kitchen appliances, etc. To make use of such opinions, it is useful to identify the polarity of the opinion, in other words, to perform sentiment classification. The goal of sentiment classification is to classify a given text/document as ...

We propose the “advanced” n-grams as a new technique for simulating user behaviour in spoken dialogue systems, and we compare it with two methods used in our prior work, i.e. linear feature combination and “normal” n-grams. All methods operate on the intention level and can incorporate speech recognition and understanding errors. In the linear feature combination model user actions (lists of 〈 ...