Arabic Spelling Correction using Supervised Learning

In this work, we address the problem of spelling correction in the Arabic language utilizing the new corpus provided by QALB (Qatar Arabic Language Bank) project which is an annotated corpus of sentences with errors and their corrections. The corpus contains edit, add before, split, merge, add after, move and other error types. We are concerned with the first four error types as they contribute more than 90% of the spelling errors in the corpus. The proposed system has many models to address each error type on its own and then integrating all the models to provide an efficient and robust system that achieves an overall recall of 0.59, precision of 0.58 and F1 score of 0.58 including all the error types on the development set. Our system participated in the QALB 2014 shared task "Automatic Arabic Error Correction" and achieved an F1 score of 0.6, earning the sixth place out of nine participants.Comment: System description paper that is submitted in the EMNLP 2014 conference shared task "Automatic Arabic Error Correction" (Mohit et al., 2014) in the Arabic NLP workshop. 6 page

VARD2:a tool for dealing with spelling variation in historical corpora

When applying corpus linguistic techniques to historical corpora, the corpus researcher should be cautious about the results obtained. Corpus annotation techniques such as part of speech tagging, trained for modern languages, are particularly vulnerable to inaccuracy due to vocabulary and grammatical shifts in language over time. Basic corpus retrieval techniques such as frequency profiling and concordancing will also be affected, in addition to the more sophisticated techniques such as keywords, n-grams, clusters and lexical bundles which rely on word frequencies for their calculations. In this paper, we highlight these problems with particular focus on Early Modern English corpora. We also present an overview of the VARD tool, our proposed solution to this problem, which facilitates pre-processing of historical corpus data by inserting modern equivalents alongside historical spelling variants. Recent improvements to the VARD tool include the incorporation of techniques used in modern spell checking software

Social Tag-Based Recommendation Services

Recommendation systems are a staple of Web 2.0. Sites such as Amazon.com and Netflix, for example, use recommendation systems to suggest products to customers. Currently, most of these systems involve looking at numerical ratings to judge user interest. These methods are effective, but they do not take into account the context in which the users rated the objects. This project aims to develop a tag based recommendation system to take context into account. Popular websites such as del.icio.us and Citeulike.org already use this data model, but do not generate recommendations from it.The specific goal is to recommend academic papers to researchers

Improving Finite-State Spell-Checker Suggestions with Part of Speech N-Grams

In this paper we demonstrate a finite-state implementation of context-aware spell checking utilizing an N-gram based part of speech (POS) tagger to rerank the suggestions from a simple edit-distance based spell-checker. We demonstrate the benefits of context-aware spell-checking for English and Finnish and introduce modifications that are necessary to make traditional N-gram models work for morphologically more complex languages, such as Finnish.Peer reviewe

Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data

In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvestigated. Sentiment analysis approaches mostly include spell checking or rule-based normalisation as preprocess- ing and rarely investigate its impact on the task performance. We present an optimised sentiment classifier and investigate to what extent its performance can be enhanced by integrating SMT-based normalisation as preprocessing. Experiments on a test set comprising a variety of user-generated content genres revealed that normalisation improves sentiment classification performance on tweets and blog posts, showing the model’s ability to generalise to other data genres