Harmony Search Algorithm for Word Sense Disambiguation

Abstract

Word Sense Disambiguation (WSD) is the task of determining which sense of an ambiguous word (word with multiple meanings) is chosen in a particular use of that word, by considering its context. A sentence is considered ambiguous if it contains ambiguous word(s). Practically, any sentence that has been classified as ambiguous usually has multiple interpretations, but just one of them presents the correct interpretation. We propose an unsupervised method that exploits knowledge based approaches for word sense disambiguation using Harmony Search Algorithm (HSA) based on a Stanford dependencies generator (HSDG). The role of the dependency generator is to parse sentences to obtain their dependency relations. Whereas, the goal of using the HSA is to maximize the overall semantic similarity of the set of parsed words. HSA invokes a combination of semantic similarity and relatedness measurements, i.e., Jiang and Conrath (jcn) and an adapted Lesk algorithm, to perform the HSA fitness function. Our proposed method was experimented on benchmark datasets, which yielded results comparable to the state-of-the-art WSD methods. In order to evaluate the effectiveness of the dependency generator, we perform the same methodology without the parser, but with a window of words. The empirical results demonstrate that the proposed method is able to produce effective solutions for most instances of the datasets used.

Word sense disambiguation using hybrid swarm intelligence approach

“…TSP–ACO obtains the best F-measure of 62.90%, which is lower than the value in the current work for the same corpora (SensEval-2). Moreover, Abed [9] reported an F-measure accuracy of 60.69% using (HSA), whereas our technique obtained improved accuracy at 65.83%. In practice, Abed [9] had been using Stanford Parser as a parsing method to gain additional grammatical relation information whereas our method gains better accuracy without the parsing phase.…”

“…Moreover, Abed [9] reported an F-measure accuracy of 60.69% using (HSA), whereas our technique obtained improved accuracy at 65.83%. In practice, Abed [9] had been using Stanford Parser as a parsing method to gain additional grammatical relation information whereas our method gains better accuracy without the parsing phase. In addition, the new version of GA, namely, self-adaptive GA, [38] obtains only 51.49% accuracy.…”

A Systematic Review on Harmony Search Algorithm: Theory, Literature, and Applications

“…e branch information was used to determine the weight of each branch. Abed et al [149] developed an unsupervised method for word disambiguation using HSA. HSA-based dependency generator was used to parse sentences.…”

Extractive multi document summarization using harmony search algorithm

“…The HMCR plays an important role in selecting a value from memory while the PA is important for both exploitation and exploration. The exploitation is used to find optimal solutions, whereas exploration is used to avoid local minima [31]. The following algorithm shows how HSA is used for text summarization.…”