The probability of

untagged text in the training

probabilistic model to tag English text

Recursive models

Recurrent model

recursive networks

n many cases, this means encodingthe structure as a xed width vector, which we can then pass on to another statisticallearner for further processing. While convolutional and pooling architectures allow us toencode arbitrary large items as xed size vectors capturing their most salient features,they do so by sacricing most of the structural information.

Convolutional and pooling architecture show promising resultson many tasks, including document classication,7short-text categorization,8sentimentclassication,9relation type classication between entities,10event detection,11paraphraseidentication,12semantic role labeling,13question answering,14predicting box-oce rev-

CGsupertagging,3dialog state tracking,4pre-ordering for statistical machine translation5andlanguage modeling.6

convolutional and pooling layers (Section 9) are useful for classicationtasks in which we expect to nd strong local clues regarding class membership,

Mathematical

feed-forward networks and recurrent /recursive networks

generation of exercises and tests.

Most NLP research is developed and optimized for native-language material, and it is easier to obtain enough annotated language material to train the statistical models and machine-learning approaches used in current research and development, so that in principle a wide range of NLP tools with high-quality analysis is available

recognizing textual entailment and paraphrase recognition (Androutsopoulos & Malakasiotis, 2010

context patterns

standard parsing algorithm

The idea of constraint relaxation is to eliminate certain constraints from the grammar, for example, specifi cations ensuring agreement, thereby allowing the grammar to license more strings than before. This assumes that an error can be mapped to a particular constraint to be relaxed,

Starting with a standard native-language grammar, rules are added to license strings which are used by language learners but not in the native language,

ince learners need more scaffolding than a list of alternatives from which to choose

“in contrast to most misspellings by native writers, many L2 misspellings are multiple-edit errors and are thus not corrected by a spell checker designed for native writers.”

All approaches to detecting and diagnosing learner errors must explicitly or implicitly model the space of well-formed and ill-formed variation that is possible given a particular activity and a given learner.

traditionally focused on learner errors

it is necessary to abstract away from the specifi c string entered by the learner to more general classes of properties by automatically analyzing the learner input using NLP algorithms and resources.

Jurafsky and Martin (2009).

e-Tutor (Heift, 2010), Robo-Sensei (Nagata, 2009), and TAGARELA (Amaral & Meurers, 2011)

NLP technology in ICALL systems that are fully integrated into language programs.Robo-Sensei(Nagata, 2002), andE-Tutor(Heift, 1998, 2003) are two such successfulexamples. There is also a third one,Spanish for Business Professionals(Hagen, 1999)

Being able to processill-formed input is only part of the challenge of designing real-life ICALL systems.Issues such as activity design, language assessment and measurement, teachingtechniques, syllabus design, second language analysis, cognitive models of secondlanguage acquisition, and language policy and planning are important for the designof ICALL systems for real-life FLTL

To determine the potential role of ICALL systems in FLTL

they were very receptive to theidea of automatic support tools to practice receptive skills, reinforce the acquisition oflanguage forms, propose remedial work, and raise linguistic awareness

. On the other hand, the same instructorsperceive form-based activities as problematic for use in the classroom because theycan reduce the pace of the lesson and take away time that could be dedicated tomeaning-based, communicative activities. In such a setting, the amount of time astudent spends in a class with a language instructor is very limited, and individualinteraction between instructor and student even more scarce. The consequence is thatclassroom time is often used for meaning-based activities, and work on linguisticcategories and rules is de-emphasized and confined to homework.