N-граммды модельдер

We describe a corpus-based induction algo- rithm for probabilistic context-free gram- mars. The algorithm employs a greedy heuristic search within a Bayesian frame- work, and a post-pass using the Inside- …...

We describe, analyze, and experimentally evaluate a new probabilistic model for word- sequence prediction in natural languages, based on prediction suffi~v trees (PSTs). By using efficient data structures, we extend …...

We present an empirical study of the applica- bility of Probabilistic Lexicalized Tree Inser- tion Grammars (PLTIG), a lexicalized counter- part to Probabilistic Context-Free Grammars (PCFG), to problems in stochastic …...

Language modeling is to associate a sequence of words with a priori probability, which is a key part of many natural language applications such as speech recognition and statistical ma- …...

This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction and Thai-English language identification. The paper also proposes rule-reduction algorithm applying mutual information to …...

We present the first application of the head-driven statistical parsing model of Collins (1999) as a si- multaneous language model and parser for large- vocabulary speech recognition. The model is …...

This paper shows that discriminative reranking with an averaged perceptron model yields substantial improvements in realization quality with CCG. The paper confirms the utility of including language model log probabilities …...

Standard phrase-based translation models do not explicitly model context dependence be- tween translation units. As a result, they rely on large phrase pairs and target language mod- els to recover …...

We present a novel approach for analysing and classifying lyrics, experimenting both with n- gram models and more sophisticated features that model different dimensions of a song text, such as …...

We present STIR (STrongly Incremen- tal Repair detection), a system that de- tects speech repairs and edit terms on transcripts incrementally with minimal la- tency. STIR uses information-theoretic measures from …...

We propose a conversion of bilingual sentence pairs and the corresponding word alignments into novel linear se- quences. These are joint translation and reordering (JTR) uniquely defined sequences, combining interdepending …...

While it has been established that transi- tions between discourse relations are im- portant for coherence, such information has not so far been used to aid in lan- guage generation. …...

AbstractPredictive incremental parsing produces syntactic representations of sentences as they are produced, e.g. by typing or speaking. In order to generate connected parses for such unfinished sentences, upcoming word types …...

AbstractIn recent years neural language models (LMs) have set the state-of-the-art performance for several benchmarking datasets. While the reasons for their success and their computational demand are well-documented, a comparison …...