Automated grammar correction techniques have seen improvement over the years, but there is still much room for increased performance. Current correction techniques mainly focus on identifying and correcting a speciﬁc type of error, such as verb form misuse or preposition misuse, which restricts the corrections to a limited scope. We introduce a novel technique, based on a noisy channel model, which can utilize the whole sentence context to determine proper corrections.
This paper describes a noisy channel model of speech repairs, which can identify and correct repairs in speech transcripts. A syntactic parser is used as the source model, and a novel type of TAG-based transducer is the channel model. The use of TAG is motivated by the intuition that the reparandum is a “rough copy” of the repair. The model is trained and tested on the Switchboard disﬂuency-annotated corpus.
Most foreign names are transliterated into Chinese, Japanese or Korean with approximate phonetic equivalents. The transliteration is usually achieved through intermediate phonemic mapping. This paper presents a new framework that allows direct orthographical mapping (DOM) between two different languages, through a joint source-channel model, also called n-gram transliteration model (TM).
Most machine transliteration systems transliterate out of vocabulary (OOV) words through intermediate phonemic mapping. A framework has been presented that allows direct orthographical mapping between two languages that are of different origins employing different alphabet sets. A modified joint source–channel model along with a number of alternatives have been proposed. Aligned transliteration units along with their context are automatically derived from a bilingual training corpus to generate the collocational statistics. ...
The source model is used to estimate the generative probability of a word sequence, in which each word belongs to one word type. For each word type, a channel model is used to estimate the generative probability of a character string given the word type. So there are multiple channel models. We shall show in this paper that our models provide a statistical framework to corporate a wide variety linguistic knowledge and statistical models in a unified way. We evaluate the performance of our system using an annotated test set. ...
We present a document compression system that uses a hierarchical noisy-channel model of text production. Our compression system ﬁrst automatically derives the syntactic structure of each sentence and the overall discourse structure of the text given as input. The system then uses a statistical hierarchical model of text production in order to drop non-important syntactic and discourse constituents so as to generate coherent, grammatical document compressions of arbitrary length.
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Ultra-Wideband Source Localization Using a Particle-Swarm-Optimized Capon Estimator from a Frequency-Dependent Channel Modeling Viewpoint
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài:
Research Article Propagation in Tunnels: Experimental Investigations and Channel Modeling in a Wide
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Time-Variant MIMO Channel Model Directly Parametrised from Measurements
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Investigations in Satellite MIMO Channel Modeling: Accent on Polarization
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Spatial-Temporal Correlation Properties of the 3GPP Spatial Channel Model and the Kronecker MIMO Channel Model