Extended Edit Distance Research Articles

Matrices inducing generalized metric on sequences

Sequence comparison is a basic task to capture similarities and differences between two or more sequences of symbols, with countless applications such as in computational biology. An alignment is a way to compare sequences, where a giving scoring function determines the degree of similarity between them. Many scoring functions are obtained from scoring matrices. However,not all scoring matrices induce scoring functions which are distances, since the scoring function is not necessarily a metric. In this work we establish necessary and sufficient conditions for scoring matrices to induce each one of the properties of a metric in weighted edit distances. For a subset of scoring matrices that induce normalized edit distances, we also characterize each class of scoring matrices inducing normalized edit distances. Furthermore, we define an extended edit distance, which takes into account a set of editing operations that transforms one sequence into another regardless of the existence of a usual corresponding alignment to represent them, describing a criterion to find a sequence of edit operations whose weight is minimum. Similarly, we determine the class of scoring matrices that induces extended edit distances for each of the properties of a metric.

Dynamic Editing Distance-based Extracting Relevant Information Approach from Social Networks

Online social networks, such as Facebook, Twitter, LinkedIn, etc., have grown exponentially in recent times with a large amount of information. These social networks have huge volumes of data especially in structured, textual, and unstructured forms which have often led to cyber-crimes like cyber terrorism, cyber bullying, etc., and extracting information from these data has now become a serious challenge in order to ensure the data safety. In this work, we propose a new, supervised approach for Information Extraction (IE) from Web resources based on remote dynamic editing, called EIDED. Our approach is part of the family of IE approaches based on masks extraction and is articulated around three algorithms: (i) a labeling algorithm, (ii) a learning and inference algorithm, and (iii) an extended edit distance algorithm. Our proposed approach is able to work even in the presence of anomalies in the tuples such as missing attributes, multivalued attributes, permutation of attributes, and in the structure of web pages. The experimental study, which we conducted, on a standard database of web pages, shows the performance of our EIDED approach compared to approaches based on the classic edit distance, and this with respect to the standard metrics recall coefficient, precision, and F1-measure.

Natural language inference for arabic using extended tree edit distance with subtrees

Many natural language processing (NLP) applications require the computation of similarities between pairs of syntactic or semantic trees. Many researchers have used tree edit distance for this task...

Swap and mismatch edit distance

There is no known algorithm that solves the general case of theapproximate string matching problem with the extended edit distance, where the edit operations are: insertion, deletion, mismatch and swap, in timeo(nm), wheren is the length of the text andm is the length of the pattern. In an effort to study this problem, the edit operations were analysed independently. It turns out that the approximate matching problem with only the mismatch operation can be solved in timeO(n √m logm). If the only edit operation allowed is swap, then the problem can be solved in timeO(n logm logσ), whereσ=min(m, |Σ|). In this paper we show that theapproximate string matching problem withswap andmismatch as the edit operations, can be computed in timeO(n √m logm).

Efficient Algorithms for Approximate String Matching with Swaps

Most research on the edit distance problem and thek-differences problem considered the set of edit operations consisting of changes, insertions, and deletions. In this paper we include theswapoperation that interchanges two adjacent characters into the set of allowable edit operations, and we present anO(tmin(m,n))-time algorithm for the extended edit distance problem, wheretis the edit distance between the given strings, and anO(kn)-time algorithm for the extendedk-differences problem. That is, we add swaps into the set of edit operations without increasing the time complexities of previous algorithms that consider only changes, insertions, and deletions for the edit distance andk-differences problems.