The Porter stemming algorithm: then and now

Purpose: In 1980, Porter presented a simple algorithm for stemming English language words. This paper summarises the main features of the algorithm, and highlights its role not just in modern information retrieval research, but also in a range of related subject domains. Design: Review of literature and research involving use of the Porter algorithm. Findings: The algorithm has been widely adopted and extended so that it has become the standard approach to word conflation for information retrieval in a wide range of languages. Value: The 1980 paper in Program by Porter describing his algorithm has been highly cited. This paper provides a context for the original paper as well as an overview of its subsequent use

Enhanced text stemmer for standard and non-standard word patterns in Malay texts

Text stemming is a useful language preprocessing tool in the field of information retrieval, text classification and natural language processing. A text stemmer is a computer program that removes affixes, clitics and particles to obtain the root words from the derived words. Over the past few years, few text stemmers have been developed for the Malay language but unfortunately, these text stemmers suffer from various stemming errors. It is due to the difficulty in dealing with the complexity of the Malay language morphological rules. These text stemmers are developed for text stemming against affixation words only whereas there are other affixation, reduplication and compounding words in the Malay language. Furthermore, none of these text stemmers has been developed for text stemming against social media texts which comprise of the non-standard derived words. Therefore, this research study aims to improve the existing text stemmers capability of stemming affixation, reduplication and compounding words while minimising the possible stemming errors. Moreover, this research study also aims to address text stemming process for non-standard derived words on the social media platforms by removing non-standard affixes, clitics and particles. This research study adopts a multiple text stemming approach that use affix removal method and dictionary lookup in specific arrangement order to correctly stem standard and non-standard affixation, reduplication and compounding words in the standard texts and social media texts. The proposed text stemmer is evaluated against various text documents using the direct evaluation method and the text classification is used as the indirect evaluation method to validate the effectiveness of the proposed enhanced text stemmer. In general, the proposed enhanced text stemmer outperforms the baseline text stemmer. The stemming accuracy of the proposed enhanced text stemmer achieves an average of 98.7% against the standard texts and an average of 73.7% against the social media texts. Meanwhile, the performance of the proposed enhanced text stemmer in the sports news classification application achieves an average of 85% accuracy and the illicit content classification application achieves an average of 75% accuracy. Meanwhile, the baseline text stemmer achieves an average of 63.5% stemming accuracy against the standard texts but unfortunately, it is unable to stem non-standard derived words in the social media texts. The baseline text stemmer performs poorly in sports news classification and illicit content classification with an average accuracy of 78% and 63% respectively. In short, the experimental results suggest that the proposed enhanced text stemmer has promising stemming accuracy for text stemming against the standard texts and social media texts. It also influences the performance of the text classification application

Development of a stemmer for the isiXhosa language

IsiXhosa language is one of the eleven official languages and the second most widely spoken language in South Africa. However, in terms of computational linguistics, the language did not get attention and natural language related work is almost non-existent. Document retrieval using unstructured queries requires some kind of language processing, and an efficient retrieval of documents can be achieved if we use a technique called stemming. The area that involves document storage and retrieval is called Information Retrieval (IR). Basically, IR systems make use of a Stemmer to index document representations and also terms in users’ queries to retrieve matching documents. In this dissertation, we present the developed Stemmer that can be used in both conditions. The Stemmer is used in IR systems, like Google to retrieve documents written in isiXhosa. In the Eastern Cape Province of South Africa many public schools take isiXhosa as a subject and also a number of Universities in South Africa teach isiXhosa. Therefore, for a language important such as this, it is important to make valuable information that is available online accessible to users through the use of IR systems. In our efforts to develop a Stemmer for the isiXhosa language, an investigation on how others have developed Stemmers for other languages was carried out. From the investigation we came to realize that the Porter stemming algorithm in particular was the main algorithm that many of other Stemmers make use of as a reference. We found that Porter’s algorithm could not be used in its totality in the development of the isiXhosa Stemmer because of the morphological complexity of the language. We developed an affix removal that is embedded with rules that determine which order should be followed in stripping the affixes. The rule is that, the word under consideration is checked against the exceptions, if it’s not in the exceptions list then the stripping continue in the following order; Prefix removal, Suffix removal and finally save the result as stem. The Stemmer was successfully developed and was tested and evaluated in a sample data that was randomly collected from the isiXhosa text books and isiXhosa dictionary. From the results obtained we concluded that the Stemmer can be used in IR systems as it showed 91 percent accuracy. The errors were 9 percent and therefore these results are within the accepted range and therefore the Stemmer can be used to help in retrieval of isiXhosa documents. This is only a noun Stemmer and in the future it can be extended to also stem verbs as well. The Stemmer can also be used in the development of spell-checkers of isiXhosa

Development of a stemmer for the isiXhosa language

IsiXhosa language is one of the eleven official languages and the second most widely spoken language in South Africa. However, in terms of computational linguistics, the language did not get attention and natural language related work is almost non-existent. Document retrieval using unstructured queries requires some kind of language processing, and an efficient retrieval of documents can be achieved if we use a technique called stemming. The area that involves document storage and retrieval is called Information Retrieval (IR). Basically, IR systems make use of a Stemmer to index document representations and also terms in users’ queries to retrieve matching documents. In this dissertation, we present the developed Stemmer that can be used in both conditions. The Stemmer is used in IR systems, like Google to retrieve documents written in isiXhosa. In the Eastern Cape Province of South Africa many public schools take isiXhosa as a subject and also a number of Universities in South Africa teach isiXhosa. Therefore, for a language important such as this, it is important to make valuable information that is available online accessible to users through the use of IR systems. In our efforts to develop a Stemmer for the isiXhosa language, an investigation on how others have developed Stemmers for other languages was carried out. From the investigation we came to realize that the Porter stemming algorithm in particular was the main algorithm that many of other Stemmers make use of as a reference. We found that Porter’s algorithm could not be used in its totality in the development of the isiXhosa Stemmer because of the morphological complexity of the language. We developed an affix removal that is embedded with rules that determine which order should be followed in stripping the affixes. The rule is that, the word under consideration is checked against the exceptions, if it’s not in the exceptions list then the stripping continue in the following order; Prefix removal, Suffix removal and finally save the result as stem. The Stemmer was successfully developed and was tested and evaluated in a sample data that was randomly collected from the isiXhosa text books and isiXhosa dictionary. From the results obtained we concluded that the Stemmer can be used in IR systems as it showed 91 percent accuracy. The errors were 9 percent and therefore these results are within the accepted range and therefore the Stemmer can be used to help in retrieval of isiXhosa documents. This is only a noun Stemmer and in the future it can be extended to also stem verbs as well. The Stemmer can also be used in the development of spell-checkers of isiXhosa

ALGORITMA STEMMING TEKS BAHASA PASER KALIMANTAN TIMUR BERBASIS ATURAN TATA BAHASA

Bahasa Paser merupakan bahasa daerah yang digunakan oleh masyarakat Kalimantan Timur. Pada dokumen morfologi bahasa Paser ditemukan kata yang tidak ada pada kamus. Hal ini menjadi masalah bagi orang awam yang ingin mempelajari bahasa Paser. Algoritma stemming merupakan proses pemotongan imbuhan pada kata yang berimbuhan menjadi kata dasar sesuai dengan aturan tata bahasa dan pencocokan kata pada database kamus sehingga menghasilkan kata dasar. Hasil akurasi dari algoritma stemming bahasa Paser terhadap 557 kata uji adalah 93.9%. Kegagalan dari hasil pengujian disebabkan karena banyak kata yang tidak tersedia pada database kamus. Hasil pengujian sistem membuktikan bahwa algoritma stemming bahasa Paser berjalan dengan baik sesuai dengan aturan tata bahasa Paser

ANNOTATED DISJUNCT FOR MACHINE TRANSLATION

Most information found in the Internet is available in English version. However, most people in the world are non-English speaker. Hence, it will be of great advantage to have reliable Machine Translation tool for those people. There are many approaches for developing Machine Translation (MT) systems, some of them are direct, rule-based/transfer, interlingua, and statistical approaches. This thesis focuses on developing an MT for less resourced languages i.e. languages that do not have available grammar formalism, parser, and corpus, such as some languages in South East Asia. The nonexistence of bilingual corpora motivates us to use direct or transfer approaches. Moreover, the unavailability of grammar formalism and parser in the target languages motivates us to develop a hybrid between direct and transfer approaches. This hybrid approach is referred as a hybrid transfer approach. This approach uses the Annotated Disjunct (ADJ) method. This method, based on Link Grammar (LG) formalism, can theoretically handle one-to-one, many-to-one, and many-to-many word(s) translations. This method consists of transfer rules module which maps source words in a source sentence (SS) into target words in correct position in a target sentence (TS). The developed transfer rules are demonstrated on English → Indonesian translation tasks. An experimental evaluation is conducted to measure the performance of the developed system over available English-Indonesian MT systems. The developed ADJ-based MT system translated simple, compound, and complex English sentences in present, present continuous, present perfect, past, past perfect, and future tenses with better precision than other systems, with the accuracy of 71.17% in Subjective Sentence Error Rate metric

Classifying the suras by their lexical semantics :an exploratory multivariate analysis approach to understanding the Qur'an

PhD ThesisThe Qur'an is at the heart of Islamic culture. Careful, well-informed interpretation of it is fundamental both to the faith of millions of Muslims throughout the world, and also to the non-Islamic world's understanding of their religion. There is a long and venerable tradition of Qur'anic interpretation, and it has necessarily been based on literary-historical methods for exegesis of hand-written and printed text. Developments in electronic text representation and analysis since the second half of the twentieth century now offer the opportunity to supplement traditional techniques by applying the newly-emergent computational technology of exploratory multivariate analysis to interpretation of the Qur'an. The general aim of the present discussion is to take up that opportunity. Specifically, the discussion develops and applies a methodology for discovering the thematic structure of the Qur'an based on a fundamental idea in a range of computationally oriented disciplines: that, with respect to some collection of texts, the lexical frequency profiles of the individual texts are a good indicator of their semantic content, and thus provide a reliable criterion for their conceptual categorization relative to one another. This idea is applied to the discovery of thematic interrelationships among the suras that constitute the Qur'an by abstracting lexical frequency data from them and then analyzing that data using exploratory multivariate methods in the hope that this will generate hypotheses about the thematic structure of the Qur'an. The discussion is in eight main parts. The first part introduces the discussion. The second gives an overview of the structure and thematic content of the Qur'an and of the tradition of Qur'anic scholarship devoted to its interpretation. The third part xvi defines the research question to be addressed together with a methodology for doing so. The fourth reviews the existing literature on the research question. The fifth outlines general principles of data creation and applies them to creation of the data on which the analysis of the Qur'an in this study is based. The sixth outlines general principles of exploratory multivariate analysis, describes in detail the analytical methods selected for use, and applies them to the data created in part five. The seventh part interprets the results of the analyses conducted in part six with reference to the existing results in Qur'anic interpretation described in part two. And, finally, the eighth part draws conclusions relative to the research question and identifies directions along which the work presented in this study can be developed

ANNOTATED DISJUNCT FOR MACHINE TRANSLATION

Most information found in the Internet is available in English version. However, most people in the world are non-English speaker. Hence, it will be of great advantage to have reliable Machine Translation tool for those people. There are many approaches for developing Machine Translation (MT) systems, some of them are direct, rule-based/transfer, interlingua, and statistical approaches. This thesis focuses on developing an MT for less resourced languages i.e. languages that do not have available grammar formalism, parser, and corpus, such as some languages in South East Asia. The nonexistence of bilingual corpora motivates us to use direct or transfer approaches. Moreover, the unavailability of grammar formalism and parser in the target languages motivates us to develop a hybrid between direct and transfer approaches. This hybrid approach is referred as a hybrid transfer approach. This approach uses the Annotated Disjunct (ADJ) method. This method, based on Link Grammar (LG) formalism, can theoretically handle one-to-one, many-to-one, and many-to-many word(s) translations. This method consists of transfer rules module which maps source words in a source sentence (SS) into target words in correct position in a target sentence (TS). The developed transfer rules are demonstrated on English → Indonesian translation tasks. An experimental evaluation is conducted to measure the performance of the developed system over available English-Indonesian MT systems. The developed ADJ-based MT system translated simple, compound, and complex English sentences in present, present continuous, present perfect, past, past perfect, and future tenses with better precision than other systems, with the accuracy of 71.17% in Subjective Sentence Error Rate metric

Efficient query expansion

Hundreds of millions of users each day search the web and other repositories to meet their information needs. However, queries can fail to find documents due to a mismatch in terminology. Query expansion seeks to address this problem by automatically adding terms from highly ranked documents to the query. While query expansion has been shown to be effective at improving query performance, the gain in effectiveness comes at a cost: expansion is slow and resource-intensive. Current techniques for query expansion use fixed values for key parameters, determined by tuning on test collections. We show that these parameters may not be generally applicable, and, more significantly, that the assumption that the same parameter settings can be used for all queries is invalid. Using detailed experiments, we demonstrate that new methods for choosing parameters must be found. In conventional approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. We demonstrate a new method of obtaining expansion terms, based on past user queries that are associated with documents in the collection. The most effective query expansion methods rely on costly retrieval and processing of feedback documents. We explore alternative methods for reducing query-evaluation costs, and propose a new method based on keeping a brief summary of each document in memory. This method allows query expansion to proceed three times faster than previously, while approximating the effectiveness of standard expansion. We investigate the use of document expansion, in which documents are augmented with related terms extracted from the corpus during indexing, as an alternative to query expansion. The overheads at query time are small. We propose and explore a range of corpus-based document expansion techniques and compare them to corpus-based query expansion on TREC data. These experiments show that document expansion delivers at best limited benefits, while query expansion, including standard techniques and efficient approaches described in recent work, usually delivers good gains. We conclude that document expansion is unpromising, but it is likely that the efficiency of query expansion can be further improved

Effective techniques for Indonesian text retrieval

The Web is a vast repository of data, and information on almost any subject can be found with the aid of search engines. Although the Web is international, the majority of research on finding of information has a focus on languages such as English and Chinese. In this thesis, we investigate information retrieval techniques for Indonesian. Although Indonesia is the fourth most populous country in the world, little attention has been given to search of Indonesian documents. Stemming is the process of reducing morphological variants of a word to a common stem form. Previous research has shown that stemming is language-dependent. Although several stemming algorithms have been proposed for Indonesian, there is no consensus on which gives better performance. We empirically explore these algorithms, showing that even the best algorithm still has scope for improvement. We propose novel extensions to this algorithm and develop a new Indonesian stemmer, and show that these can improve stemming correctness by up to three percentage points; our approach makes less than one error in thirty-eight words. We propose a range of techniques to enhance the performance of Indonesian information retrieval. These techniques include: stopping; sub-word tokenisation; and identification of proper nouns; and modifications to existing similarity functions. Our experiments show that many of these techniques can increase retrieval performance, with the highest increase achieved when we use grams of size five to tokenise words. We also present an effective method for identifying the language of a document; this allows various information retrieval techniques to be applied selectively depending on the language of target documents. We also address the problem of automatic creation of parallel corpora --- collections of documents that are the direct translations of each other --- which are essential for cross-lingual information retrieval tasks. Well-curated parallel corpora are rare, and for many languages, such as Indonesian, do not exist at all. We describe algorithms that we have developed to automatically identify parallel documents for Indonesian and English. Unlike most current approaches, which consider only the context and structure of the documents, our approach is based on the document content itself. Our algorithms do not make any prior assumptions about the documents, and are based on the Needleman-Wunsch algorithm for global alignment of protein sequences. Our approach works well in identifying Indonesian-English parallel documents, especially when no translation is performed. It can increase the separation value, a measure to discriminate good matches of parallel documents from bad matches, by approximately ten percentage points. We also investigate the applicability of our identification algorithms for other languages that use the Latin alphabet. Our experiments show that, with minor modifications, our alignment methods are effective for English-French, English-German, and French-German corpora, especially when the documents are not translated. Our technique can increase the separation value for the European corpus by up to twenty-eight percentage points. Together, these results provide a substantial advance in understanding techniques that can be applied for effective Indonesian text retrieval