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Harmony Search Algorithm for Word Sense Disambiguation.

Abed SA, Tiun S, Omar N - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Knowledge Technology Research Group (KT), Centre for Artificial Intelligent (CAIT), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.

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.

No MeSH data available.


Harmony memory initialisation.The pseudo code of initialising the harmony memory of HSA for WSD.
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pone.0136614.g004: Harmony memory initialisation.The pseudo code of initialising the harmony memory of HSA for WSD.

Mentions: Step 1: Initialise the Harmony Memory (HM). This step initialises the HM with a number of solutions (sets of senses) with respect to the HMS value. Due to the necessity of covering all senses for each word, we initialised the HM by the pseudo code shown in Fig 4. Moreover, the harmony memory holds a solution that presents the most frequent sense for each word to improve the successors solutions without being the final solution.


Harmony Search Algorithm for Word Sense Disambiguation.

Abed SA, Tiun S, Omar N - PLoS ONE (2015)

Harmony memory initialisation.The pseudo code of initialising the harmony memory of HSA for WSD.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4589330&req=5

pone.0136614.g004: Harmony memory initialisation.The pseudo code of initialising the harmony memory of HSA for WSD.
Mentions: Step 1: Initialise the Harmony Memory (HM). This step initialises the HM with a number of solutions (sets of senses) with respect to the HMS value. Due to the necessity of covering all senses for each word, we initialised the HM by the pseudo code shown in Fig 4. Moreover, the harmony memory holds a solution that presents the most frequent sense for each word to improve the successors solutions without being the final solution.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Knowledge Technology Research Group (KT), Centre for Artificial Intelligent (CAIT), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.

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.

No MeSH data available.