This paper reports on experiments to improve the Optical Character Recognition (ocr) quality of historical text as a preliminary step in text mining. We analyse the quality of ocred text compared to a gold standard and show how it can be improved by performing two automatic correction steps. We also demonstrate the impact this can have on named entity recognition in a preliminary extrinsic eval...

This paper describes a mobile device which tries to give the blind or visually impaired access to text information. Three key technologies are required for this system: text detection, optical character recognition, and speech synthesis. Blind users and the mobile environment imply two strong constraints. First, pictures will be taken without control on camera settings and a priori information ...

In this paper, we describe the University of Illinois system that participated in Helping Our Own (HOO), a shared task in text correction. We target several common errors, such as articles, prepositions, word choice, and punctuation errors, and we describe the approaches taken to address each error type. Our system is based on a combination of classifiers, combined with adaptation techniques fo...

This paper describes our use of phrasebased statistical machine translation (PBSMT) for the automatic correction of errors in learner text in our submission to the CoNLL 2013 Shared Task on Grammatical Error Correction. Since the limited training data provided for the task was insufficient for training an effective SMT system, we also explored alternative ways of generating pairs of incorrect a...

A system for writer identification based on handwritten text lines is described in this paper. The system uses Hidden Markov Model based recognizers which are designed for text line recognition. Features are extracted from a text line and used to train the recognizers. Prior to feature extraction, normalization operations are applied to a text line. On the one hand, there exists a strong correl...

The use of computer mediated communication has resulted in a new form of written text—Microtext—which is very different from well-written text. Tweets and SMS messages, which have limited length and may contain misspellings, slang, or abbreviations, are two typical examples of microtext. Microtext poses new challenges to standard natural language processing tools which are usually designed for ...

This paper presents an original text mining approach based on text segmented units, mixing data mining techniques and text linguistics. First, we describe the text units and their descriptors designed for detecting mistakes in scientific papers in English. These include text organisation, emphasizing the relative positions and the context of stylistic markers. Secondly, the paper explains the t...

This paper provides a new method to correct annotation errors in a treebank. The previous error correction method constructs a pseudo parallel corpus where incorrect partial parse trees are paired with correct ones, and extracts error correction rules from the parallel corpus. By applying these rules to a treebank, the method corrects errors. However, this method does not achieve wide coverage ...

Collocations in the sense of idiosyncratic binary lexical co-occurrences are one of the biggest challenges for any language learner. Even advanced learners make collocation mistakes in that they literally translate collocation elements from their native tongue, create new words as collocation elements, choose a wrong subcategorization for one of the elements, etc. Therefore, automatic collocati...