Metaphone Algorithm Research Articles

Pengelompokan Kata Berdasarkan Kemiripan Ucapan Pada Kamus Menggunakan Algoritma Metaphone Pada Sistem Operasi Android

Indonesia is an archipelagic country consisting of various tribes and cultures and different languages. One of them is the Sumbawa language used by the people of the western part of the island of Sumbawa. Sumbawa language has 4 dialects namely Samawa, Taliwang, Jereweh, and Tongo. In Sumbawa there are also homophonic, homonim, and homograph words. Grouping words based on the similarity of speech to the Sumbawa dictionary used Metaphone algorithm on the android operating system is discussed in this study. Metaphone algorithms can be applied to multiple languages with rules that have been modified according to the desired language characteristic. Based on the results of testing the recall and precision on the new rules Metaphone algorithm can be concluded that the grouping of words based on similarity of speech in Sumbawa language can be said to be effective. The average percentage index of the recall test is 98,97% and the precision is 78,52%.

Development of Bangla Spell and Grammar Checkers: Resource Creation and Evaluation

A spell and grammar checker is profoundly essential for diverse publications especially for Bangla language in particular as it is spoken by millions of native speakers around the world. Considering the lack of research efforts, we demonstrate the development of a comprehensive Bangla spell and grammar checker with necessary resources. At first, a full-fledged and generalised Bangla monolingual corpus comprising over 100 million words has been built by scraping reputed, diversified online sources and then an extensive Bangla lexicon consisting of over 1 million unique words has been extracted from that corpus. Based on these corpus and lexicon, we have developed a combined spell and grammar checker application that simultaneously detects distinct spelling and grammatical mistakes and provides appropriate suggestions for both as well. The spell checker uses the Double Metaphone algorithm and Edit distance based on the distributed lexicons and numerical suffix dataset to detect all types of Bangla spelling mistakes with an accuracy rate of 97.21% individually. The grammar checker detects errors based on language model probability i.e. combination of bigram and trigram, and generates suggestions based on the Cosine similarity measure with the accuracy rate of 94.29% individually. The datasets and codes used in this work are freely available at https://git.io/JzJ4w .

A correction method of word spelling mistake for English text

Spell correction is already a mature field, the need to combine advantages of different methods for better performance arises in non-word problem. A combined spell correction method is proposed in this paper, which contains Levenshtein distance for comparation between misspelled words and correct spelled words in dictionary, improved Double Metaphone algorithm that includes vowel phoneme rule sets aimed at Chinese English learners, and global vectors(GloVe) for character representation that can generate vectors in order to obtain better suggestion lists for misspelled words. Result shows that the combined approach proposed in this paper is better than phonetic correction or edit distance method only, and a comparison with two generally implemented spell check tools is done for experiment which shows that this approach is better than them in correcting misspelled words, and the success rates of suggestion lists for spelling mistakes hit the spot.

Implantation of indexing optimization technology for highly specialized terms based on Metaphone phonetical algorithm

When compiling databases, for example to meet the needs of healthcare establishments, there is quite a common problem with the introduction and further processing of names and last names of doctors and patients that are highly specialized both in terms of pronunciation and writing. This is because names and last names of people cannot be unique, their notation is not subject to any rules of phonetics, while their length in different languages may not match. With the advent of the Internet, this situation has become generally critical and can lead to that multiple copies of e-mails are sent to one address. It is possible to solve the specified problem by using phonetic algorithms for comparing words Daitch-Mokotoff, Soundex, NYSIIS, Polyphone, and Metaphone, as well as the Levenshtein and Jaro algorithms, Q-gram-based algorithms, which make it possible to find distances between words. The most widespread among them are the Soundex and Metaphone algorithms, which are designed to index the words based on their sound, taking into consideration the rules of pronunciation. By applying the Metaphone algorithm, an attempt has been made to optimize the phonetic search processes for tasks of fuzzy coincidence, for example, at data deduplication in various databases and registries, in order to reduce the number of errors of incorrect input of last names. An analysis of the most common last names reveals that some of them are of the Ukrainian or Russian origin. At the same time, the rules following which the names are pronounced and written, for example in Ukrainian, differ radically from basic algorithms for English and differ quite significantly for the Russian language. That is why a phonetic algorithm should take into consideration first of all the peculiarities in the formation of Ukrainian last names, which is of special relevance now.

Pengelompokan kata berdasarkan kemiripan ucapan pada kamus menggunakan algoritma metaphone pada sistem operasi Android

Indonesia is an archipelagic country consisting of various tribes and cultures and different languages. One of them is the Sumbawa language used by the people of the western part of the island of Sumbawa. Sumbawa language has 4 dialects namely Samawa, Taliwang, Jereweh, and Tongo. In Sumbawa there are also homophonic, homonim, and homograph words. Grouping words based on the similarity of speech to the Sumbawa dictionary used Metaphone algorithm on the android operating system is discussed in this study. Metaphone algorithms can be applied to multiple languages with rules that have been modified according to the desired language characteristic. Based on the results of testing the recall and precision on the new rules Metaphone algorithm can be concluded that the grouping of words based on similarity of speech in Sumbawa language can be said to be effective. The average percentage index of the recall test is 98,97% and the precision is 78,52%.

MetaSoundex Phonetic Matching for English and Spanish

Researchers confront major problems while searching for various kinds of data in large imprecise databases, as they are not spelled correctly or in the way they were expected to be spelled. As a result, they cannot find the word they sought. Over the years of struggle, pronunciation of words was considered as one of the practices to solve the problem effectively. The technique used to acquire words based on sounds is known as “Phonetic Matching”. Soundex was the first algorithm developed and other algorithms such as Metaphone, Caverphone, DMetaphone, Phonex etc., are also used for information retrieval in different environments. The main contribution of this paper is to analyze and implement the newly proposed MetaSoundex algorithm for fixing ill-defined data in English and Spanish languages. The newly developed MetaSoundex algorithm addresses the limitations of well-known phonetic matching techniques, Metaphone and Soundex. Specifically, the new algorithm provided results that are more accurate compared to both Soundex and Metaphone algorithms and has higher precision compared to Soundex, thus reducing the noise in the considered arena.

Automating Corpora Generation with Semantic Cleaning and Tagging of Tweets for Multi-dimensional Social Media Analytics

Developing corpora from social media content involves convoluted cleaning. In this paper we propose and implement the automation of corpora building for facilitating social media mining and analytics. This automation process incorporates: a) metadata extraction and structuring b) semantic cleaning with tagging and c) learning domain terms/entities. The implementation performs comprehensive cleaning including abbreviation and slang correction, phonetic matching using metaphone algorithm, splitting joined words and identifying/learning entities. It identifies the entities, tags them and creates/updates a knowledgebase (KB) comprising of domain terms. The corpus thus constructed, facilitates multidimensional analysis and summarization. This proposed technique was tested with an experiment in which real world streaming tweets pertaining to Indian politics were collected, structured, cleaned and tagged. The results of the automation experiment can be stated as follows: a) the tweets although primarily in English, contained at times words from the regional languages. The algorithm does not recognize these words and they are construed as domain terms. An accuracy of 85.55% was achieved in identifying the correct domain terms and entities. b) The automation required human feedback and intervention which progressively reduced and reached a figure of 18% with the update and enhancement of the KB. This paper assumes relevance because the implementation automates the entire process of collecting and cleaning the tweets and yields a corpus suitable for multifaceted analysis.

High Speed Data Retrieval from National Data Center (NDC) Reducing Time and Ignoring Spelling Error in Search Key Based on Double Metaphone Algorithm

Fast and efficient data management is one of the demanding technologies of todays aspect. This paper proposes a system which makes the working procedures of present manual system of storing and retrieving huge citizens information of Bangladesh automated and increases its effectiveness. The implemented search methodology is user friendly and efficient enough for high speed data retrieval ignoring spelling error in the input keywords used for searching a particular citizen. The main concern in this research is minimizing the total searching time for a given keyword. This can be done if we can pre-establish the idea of getting the data belonging to the searching keyword. The primary and secondary key-code generated by the Double Metaphone Algorithm for each word is used to establish that idea about the word. This algorithm is used for creating the map of the original database, through which the keyword is matched against the data.