One of the most common and persistent error types in second language writing is collocation errors, such as learn knowledge instead of gain or acquire knowledge, or make damage rather than cause damage. In this work-inprogress report, we propose a probabilistic model for suggesting corrections to lexical collocation errors. The probabilistic model incorporates three features: word association s...

Park, Sohee. 2003. Lexical Collocation Use by Korean EFL College learners. SNU Working Papers in English Linguistics and Language 2, 19-40. This study investigated Korean EFL college students' lexical collocation use. The specific research questions were as follows: 1) Which factors affect the collocational competence?; 2) Which collocation types are the most problematic to Korean EFL college l...

A Back Propagation Artificial Neural Network (BPANN) is a well-known learning algorithmpredicated on a gradient descent method that minimizes the square error involving the networkoutput and the goal of output values. In this study, 261 GPS/Leveling and 8869 gravity intensityvalues of Iran were selected, then the geoid with three methods “ellipsoidal stokes integral”,“BPANN”, and “collocation” ...

The paper proposed to use decentralized and centralized I-term neural sliding mode control of distributed parameter wastewater anaerobic digestion bioprocess plant. The bioprocess plant is described by partial differential equations simplified by means of the orthogonal collocation method and used as an input/output data generator. The obtained process data are identified by means of decentrali...

We extend a lexical knowledge-base of near-synonym differences with knowledge about their collocational behaviour. This type of knowledge is useful in the process of lexical choice between near-synonyms. We acquire collocations for the near-synonyms of interest from a corpus (only collocations with the appropriate sense and part-of-speech). For each word that collocates with a nearsynonym we us...

This paper uses an unsupervised model of grounded language acquisition to study the role that social cues play in language acquisition. The input to the model consists of (orthographically transcribed) child-directed utterances accompanied by the set of objects present in the non-linguistic context. Each object is annotated by social cues, indicating e.g., whether the caregiver is looking at or...

The paper proposed to use a Recurrent Neural Network Model (RNNM) and a dynamic Backpropagation learning for centralized identification of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The anaerobic digestion bioprocess represented a distributed parameter system, described by partial differential equations. The analytica...

This paper uses an unsupervised model of grounded language acquisition to study the role that social cues play in language acquisition. The input to the model consists of (orthographically transcribed) child-directed utterances accompanied by the set of objects present in the non-linguistic context. Each object is annotated by social cues, indicating e.g., whether the caregiver is looking at or...