The NOMAD system : expectation-based detection and correction of errors during understanding of syntactically and semantically ill-formed text

Most large text-understanding systems have been designed under the assumption that the input text will be in reasonably "neat" form (for example, newspaper stories and other edited texts). However, a great deal of natural language text (for example, memos, messages, rough drafts, conversation transcripts, etc.) have features that differ significantly from "neat" texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, unclear or ambiguous interpretation, missing crucial punctuation, etc. Our solution to these problems is to make use of expectations, based both on knowledge of surface English and on world knowledge of the situation being described. These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word senses of words with multiple meanings (ambiguity), fill in missing words (ellipsis), and resolve referents (anaphora). This method of using expectations to aid the understanding of "scruffy" texts has bee incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy ship-to-shore messages

Knowledge-based intelligent error feedback in a Spanish ICALL system

This paper describes the Spanish ICALL system ESPADA which helps language learners to improve their syntactical knowledge. The most important parts of ESPADA for the learner are a Demonstration Module and an Analysis Module. The Demonstration Module provides animated presentation of selected grammatical information. The Analysis Module is able to parse ill-formed sentences and to give adequate feedback on 28 different error types from different levels of language use (syntax, semantics, agreement). It contains a robust chart-based island parser which uses a combination of mal-rules and constraint relaxation to ensure that learner input can be analysed and appropriate error feedback can be generated

Detection is the central problem in real-word spelling correction

Real-word spelling correction differs from non-word spelling correction in its aims and its challenges. Here we show that the central problem in real-word spelling correction is detection. Methods from non-word spelling correction, which focus instead on selection among candidate corrections, do not address detection adequately, because detection is either assumed in advance or heavily constrained. As we demonstrate in this paper, merely discriminating between the intended word and a random close variation of it within the context of a sentence is a task that can be performed with high accuracy using straightforward models. Trigram models are sufficient in almost all cases. The difficulty comes when every word in the sentence is a potential error, with a large set of possible candidate corrections. Despite their strengths, trigram models cannot reliably find true errors without introducing many more, at least not when used in the obvious sequential way without added structure. The detection task exposes weakness not visible in the selection task

Justification Of Conditioning Volitional Development Of Children\u27s Mastering Their Language Skills And Functions

The article analyzes the scientific approaches of domestic scientists to understanding the problem of will and the interrelated aspects of its development in ontogenesis. When studying ways of forming volitional behavior at its first stages, special attention is drawn to the fact that the child\u27s volitional behavior arises with the emergence of the skills to build speech utterances, with which he begins to plan his activities and regulate the process of its implementation, that is, the mastering of planning and regulating functions of speech takes place. By analyzing the ways of forming language skills in preschool and early school age, structures for creation a speech utterance, the author clarified the term "expression speech" (stages of its construction) and language skills, provided for the implementation of each stage of verbal expression. Also it is justified the condition of volitional development of children mastering their language skills and functions; relationship stages of planning and regulatory functions of broadcasting in preschool and early school age. The paper presents the author\u27s functional-structural model of the optimization process of development of the planning and regulatory functions in the formation of children\u27s speech in their language skills

Holaaa!! Writin like u talk is kewl but kinda hard 4 NLP

We present work in progress aiming to build tools for the normalization of User-Generated Content (UGC). As we will see, the task requires the revisiting of the initial steps of NLP processing, since UGC (micro-blog, blog, and, generally, Web 2.0 user texts) presents a number of non-standard communicative and linguistic characteristics, and is in fact much closer to oral and colloquial language than to edited text. We present and characterize a corpus of UGC text in Spanish from three different sources: Twitter, consumer reviews and blogs. We motivate the need for UGC text normalization by analyzing the problems found when processing this type of text through a conventional language processing pipeline, particularly in the tasks of lemmatization and morphosyntactic tagging, and finally we propose a strategy for automatically normalizing UGC using a selector of correct forms on top of a pre-existing spell-checker.Postprint (published version

Judging grammaticality: experiments in sentence classification

A classifier which is capable of distinguishing a syntactically well formed sentence from a syntactically ill formed one has the potential to be useful in an L2 language-learning context. In this article, we describe a classifier which classifies English sentences as either well formed or ill formed using information gleaned from three different natural language processing techniques. We describe the issues involved in acquiring data to train such a classifier and present experimental results for this classifier on a variety of ill formed sentences. We demonstrate that (a) the combination of information from a variety of linguistic sources is helpful, (b) the trade-off between accuracy on well formed sentences and accuracy on ill formed sentences can be fine tuned by training multiple classifiers in a voting scheme, and (c) the performance of the classifier is varied, with better performance on transcribed spoken sentences produced by less advanced language learners