This paper describes a new approach to automatically learning linguistic knowledge for spelling correction. A major feature of this approach is the fact that the acquired knowledge is captured in a small set of easily understood rules, as opposed to a large set of opaque features and weights. A perspicuous representation is advantageous in order to best exploit human intuition to understand and improve upon the acquired knowledge of the system.

Because chains of semantically related words express semantic continuity, such lexical chains can play an important role in the detection of malapropisms. A malapropism is a correctly spelled word that does not fit in the context where it is used because it is the result of a spelling error on a different word that was intended. I first assume that such a word has much less probability of being inserted in any chain with other words. If this assumption is correct, words that failed to be inserted with other words can be considered as potential malapropisms. A mechanism that generates spelling replacements can then be used to generate replacement candidates. The second assumption is that whenever a spelling replacement can be inserted in a chain with other words, this replacement is likely to be the intended word for which a malapropism has been substituted. The algorithm proposed here to detect lexical chains uses the on-line thesaurus WordNet to automatically quantify semantic relatio...

This paper presents a parsing system for the detection of syntactic errors. It combines a robust partial parser which obtains the main sentence components and a finite-state parser used for the description of syntactic error patterns. The system has been tested on a corpus of real texts, containing both correct and incorrect sentences, with promising results.

This paper presents a directional advantage of n-gram modeling in terms of backward or forward n-gram modeling in Bangla. The most commonly used n-gram analysis is predominantly a forward n-gram. However in Bangla it appears that a backward n-gram is repeatedly more successful and yields more grammatical results than a forward n-gram. This paper hypothesizes that the rationale behind this success is the syntactic ordering of constituents in Bangla. Bangla is a headfinal specifier-initial language as opposed to English, which is head-initial specifier-initial. Hence in Bangla, the head comes after its argument in a phrase. If an ngram analysis begins with a head and moves backwards it will stretch to its own argument but if you move forwards then you'll probably grab the argument of another head. As probability of occurrence of heads is higher, probability of depending on a head is also higher and hence a backward n-gram will probably have a greater chance of yielding grammatical results. We carried out several experiments to compare different directional results in different applications with an advantage in the backward direction. This will prove a useful linguistic insight in terms of n-gram based analysis depending upon variations of constituent analysis.

This paper presents a directional advantage of n-gram modeling in terms of backward or forward n-gram modeling in Bangla. The most commonly used n-gram analysis is predominantly a forward n-gram. However in Bangla it appears that a backward n-gram is repeatedly more successful and yields more grammatical results than a forward n-gram. This paper hypothesizes that the rationale behind this success is the syntactic ordering of constituents in Bangla. Bangla is a head-final specifier-initial language as opposed to English, which is head-initial specifier-initial. Hence in Bangla, the head comes after its argument in a phrase. If an n-gram analysis begins with a head and moves backwards it will stretch to its own argument but if you move for-wards then you'll probably grab the argument of an-other head. As probability of occurrence of heads is higher, probability of depending on a head is also higher and hence a backward n-gram will probably have a greater chance of yielding grammatical results. We carried out several experiments to compare different directional results in different applications with an advantage in the backward direction. This will prove a useful linguistic insight in terms of n-gram based analysis depending upon variations of constituent analysis. 1.