Stemming Wikipedia. In linguistic morphology and information retrieval, stemming is the process of reducing inflected or sometimes derived words to their word stem, base or root formgenerally a written word form. The stem need not be identical to the morphological root of the word it is usually sufficient that related words map to the same stem, even if this stem is not in itself a valid root. Algorithms for stemming have been studied in computer science since the 1. Many search engines treat words with the same stem as synonyms as a kind of query expansion, a process called conflation. Stemming programs are commonly referred to as stemming algorithms or stemmers. ExampleseditA stemmer for English, for example, should identify the string cats and possibly catlike, catty etc. A stemming algorithm reduces the words fishing, fished, and fisher to the root word, fish. On the other hand, argue, argued, argues, arguing, and argus reduce to the stem argu illustrating the case where the stem is not itself a word or root but argument and arguments reduce to the stem argument. HistoryeditThe first published stemmer was written by Julie Beth Lovins in 1. This paper was remarkable for its early date and had great influence on later work in this area. A later stemmer was written by Martin Porter and was published in the July 1. Program. This stemmer was very widely used and became the de facto standard algorithm used for English stemming. Dr. Porter received the Tony Kent Strix award in 2. Many implementations of the Porter stemming algorithm were written and freely distributed however, many of these implementations contained subtle flaws. As a result, these stemmers did not match their potential. To eliminate this source of error, Martin Porter released an official free software mostly BSD licensed implementation2 of the algorithm around the year 2. 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In linguistic morphology and information retrieval, stemming is the process of reducing inflected or sometimes derived words to their word stem, base or root form. He extended this work over the next few years by building Snowball, a framework for writing stemming algorithms, and implemented an improved English stemmer together with stemmers for several other languages. AlgorithmseditThere are several types of stemming algorithms which differ in respect to performance and accuracy and how certain stemming obstacles are overcome. A simple stemmer looks up the inflected form in a lookup table. The advantages of this approach are that it is simple, fast, and easily handles exceptions. The disadvantages are that all inflected forms must be explicitly listed in the table new or unfamiliar words are not handled, even if they are perfectly regular e. Pads i. Pad, and the table may be large. For languages with simple morphology, like English, table sizes are modest, but highly inflected languages like Turkish may have hundreds of potential inflected forms for each root. A lookup approach may use preliminary part of speech tagging to avoid overstemming. The production techniqueeditThe lookup table used by a stemmer is generally produced semi automatically. For example, if the word is run, then the inverted algorithm might automatically generate the forms running, runs, runned, and runly. The last two forms are valid constructions, but they are unlikely. Suffix stripping algorithmseditSuffix stripping algorithms do not rely on a lookup table that consists of inflected forms and root form relations. Instead, a typically smaller list of rules is stored which provides a path for the algorithm, given an input word form, to find its root form. Some examples of the rules include if the word ends in ed, remove the edif the word ends in ing, remove the ingif the word ends in ly, remove the lySuffix stripping approaches enjoy the benefit of being much simpler to maintain than brute force algorithms, assuming the maintainer is sufficiently knowledgeable in the challenges of linguistics and morphology and encoding suffix stripping rules. Suffix stripping algorithms are sometimes regarded as crude given the poor performance when dealing with exceptional relations like ran and run. The solutions produced by suffix stripping algorithms are limited to those lexical categories which have well known suffixes with few exceptions. This, however, is a problem, as not all parts of speech have such a well formulated set of rules. Lemmatisation attempts to improve upon this challenge. Prefix stripping may also be implemented. Of course, not all languages use prefixing or suffixing. Additional algorithm criteriaeditSuffix stripping algorithms may differ in results for a variety of reasons. One such reason is whether the algorithm constrains whether the output word must be a real word in the given language. Some approaches do not require the word to actually exist in the language lexicon the set of all words in the language. Alternatively, some suffix stripping approaches maintain a database a large list of all known morphological word roots that exist as real words. These approaches check the list for the existence of the term prior to making a decision. Typically, if the term does not exist, alternate action is taken. This alternate action may involve several other criteria. The non existence of an output term may serve to cause the algorithm to try alternate suffix stripping rules. It can be the case that two or more suffix stripping rules apply to the same input term, which creates an ambiguity as to which rule to apply. The algorithm may assign by human hand or stochastically a priority to one rule or another. Or the algorithm may reject one rule application because it results in a non existent term whereas the other overlapping rule does not. For example, given the English term friendlies, the algorithm may identify the ies suffix and apply the appropriate rule and achieve the result of friendl. One improvement upon basic suffix stripping is the use of suffix substitution. Similar to a stripping rule, a substitution rule replaces a suffix with an alternate suffix. For example, there could exist a rule that replaces ies with y. How this affects the algorithm varies on the algorithms design. To illustrate, the algorithm may identify that both the ies suffix stripping rule as well as the suffix substitution rule apply. Since the stripping rule results in a non existent term in the lexicon, but the substitution rule does not, the substitution rule is applied instead. In this example, friendlies becomes friendly instead of friendl. Diving further into the details, a common technique is to apply rules in a cyclical fashion recursively, as computer scientists would say. After applying the suffix substitution rule in this example scenario, a second pass is made to identify matching rules on the term friendly, where the ly stripping rule is likely identified and accepted. In summary, friendlies becomes via substitution friendly which becomes via stripping friend. This example also helps illustrate the difference between a rule based approach and a brute force approach. In a brute force approach, the algorithm would search for friendlies in the set of hundreds of thousands of inflected word forms and ideally find the corresponding root form friend. In the rule based approach, the three rules mentioned above would be applied in succession to converge on the same solution. Motherboard Manual Dell. Chances are that the rule based approach would be slower, as lookup algorithms have a direct access to the solution, while rule based should try several options, and combinations of them, and then choose which result seems to be the best. Lemmatisation algorithmseditA more complex approach to the problem of determining a stem of a word is lemmatisation. This process involves first determining the part of speech of a word, and applying different normalization rules for each part of speech.