Information retrieval systems (IRS) are designed to search for relevant information in large documentary databases. This information can be of various kinds, with the queries ranging from “Find all the documents containing the word conjugar” to “Find information on the conjugation of Spanish verbs”. Accordingly, various systems use different methods of search.

The earliest IRSs were developed to search for scientific articles on a specific topic. Usually, the scientists supply their papers with a set of keywords, i.e., the terms they consider most important and relevant for the topic of the paper. For example, español, verbos, subjuntivo might be the keyword set of the article “On means of expressing unreal conditions” in a Spanish scientific journal.

These sets of keywords are attached to the document in the bibliographic database of the IRS, being physically kept together with the corresponding documents or separately from them. In the simplest case, the query should explicitly contain one or more of such keywords as the condition on what the article can be found and retrieved from the database. Here is an example of a query: “Find the documents on verbos and español”. In a more elaborate system, a query can be a longer logical expression with the operators and, or, not, e.g.: “Find the documents on (sustantivos or adjetivos) and (not inglés)”.

Nowadays, a simple but powerful approach to the format of the query is becoming popular in IRSs for non-professional users: the query is still a set of words; the system first tries to find the documents containing all of these words, then all but one, etc., and finally those containing only one of the words. Thus, the set of keywords is considered in a step-by-step transition from conjunction to disjunction of their occurrences. The results are ordered by degree of relevance, which can be measured by the number of relevant keywords found in the document. The documents containing more keywords are presented to the user first.

In some systems the user can manually set a threshold for the number of the keywords present in the documents, i.e., to search for “at least m of n” keywords. With m = n, often too few documents, if any, are retrieved and many relevant documents are not found; with m = 1, too many unrelated ones are retrieved because of a high rate of false alarms.

Usually, recall and precision are considered the main characteristics of IRSs. Recall is the ratio of the number of relevant documents found divided by the total number of relevant documents in the database. Precision is the ratio of the number of relevant documents divided by the total number of documents found.

It is easy to see that these characteristics are contradictory in the general case, i.e. the greater one of them the lesser another, so that it is necessary to keep a proper balance between them.

In a specialized IRS, there usually exists an automated indexing subsystem, which works before the searches are executed. Given a set of keywords, it adds, using the or operator, other related keywords, based on a hierarchical system of the scientific, technical or business terms. This kind of hierarchical systems is usually called thesaurus in the literature on IRSs and it can be an integral part of the IRS. For instance, given the query “Find the documents on conjugación,” such a system could add the word morfología to both the query and the set of keywords in the example above, and hence find the requested article in this way.

Thus, a sufficiently sophisticated IRS first enriches the sets of keywords given in the query, and then compares this set with the previously enriched sets of keywords attached to each document in the database. Such comparison is performed according to any criteria mentioned above. After the enrichment, the average recall of the IRS system is usually increased.

Recently, systems have been created that can automatically build sets of keywords given just the full text of the document. Such systems do not require the authors of the documents to specifically provide the keywords. Some of the modern Internet search engines are essentially based on this idea.

Three decades ago, the problem of automatic extraction of keywords was called automatic abstracting. The problem is not simple, even when it is solved by purely statistical methods. Indeed, the most frequent words in any business, scientific or technical texts are purely auxiliary, like prepositions or auxiliary verbs. They do not reflect the essence of the text and are not usually taken for abstracting. However, the border between auxiliary and meaningful words cannot be strictly defined. Moreover, there exist many term-forming words like system, device, etc., which can seldom be used for information retrieval because their meaning is too general. Therefore, they are not useful for abstracts.

The multiplicity of IRSs is considered now as an important class of the applied software and, specifically, of applied linguistic systems. The period when they used only individual words as keys has passed. Developers now try to use word combinations and phrases, as well as more complicated strategies of search. The limiting factors for the more sophisticated techniques turned out to be the same as those for grammar and style checkers: the absence of complete grammatical and semantic analysis of the text of documents. The methods used now even in the most sophisticated Internet search engines are not efficient for accurate information retrieval. This leads to a high level of information noise, i.e., delivering of irrelevant documents, as well as to the frequent missing of relevant ones.

The results of retrieval operations directly depend on the quality and performance of the indexing and comparing subsystems, on the content of the terminological system or the thesaurus, and other data and knowledge used by the system. Obviously, the main tools and data sets used by an IRS have the linguistic nature.

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