Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.
Word-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication, given the pervasive polysemy in natural language. In computational linguistics, it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference.
An image retrieval system is a computer system used for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, title or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools.
Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language.
Machine translation can use a method based on dictionary entries, which means that the words will be translated as a dictionary does – word by word, usually without much correlation of meaning between them. Dictionary lookups may be done with or without morphological analysis or lemmatisation. While this approach to machine translation is probably the least sophisticated, dictionary-based machine translation is ideally suitable for the translation of long lists of phrases on the subsentential level, e.g. inventories or simple catalogs of products and services.
The Text REtrieval Conference (TREC) is an ongoing series of workshops focusing on a list of different information retrieval (IR) research areas, or tracks. It is co-sponsored by the National Institute of Standards and Technology (NIST) and the Intelligence Advanced Research Projects Activity, and began in 1992 as part of the TIPSTER Text program. Its purpose is to support and encourage research within the information retrieval community by providing the infrastructure necessary for large-scale evaluation of text retrieval methodologies and to increase the speed of lab-to-product transfer of technology.
Relevance feedback is a feature of some information retrieval systems. The idea behind relevance feedback is to take the results that are initially returned from a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types of feedback: explicit feedback, implicit feedback, and blind or "pseudo" feedback.
The EXtensible Cross-Linguistic Automatic Information Machine (EXCLAIM) was an integrated tool for cross-language information retrieval (CLIR), created at the University of California, Santa Cruz in early 2006, with some support for more than a dozen languages. The lead developers were Justin Nuger and Jesse Saba Kirchner.
Geographic information retrieval (GIR) or geographical information retrieval systems are search tools for searching the Web, enterprise documents, and mobile local search that combine traditional text-based queries with location querying, such as a map or placenames. Like traditional information retrieval systems, GIR systems index text and information from structured and unstructured documents, and also augment those indices with geographic information. The development and engineering of GIR systems aims to build systems that can reliably answer queries that include a geographic dimension, such as "What wars were fought in Greece?" or "restaurants in Beirut". Semantic similarity and word-sense disambiguation are important components of GIR. To identify place names, GIR systems often rely on natural language processing or other metadata to associate text documents with locations. Such georeferencing, geotagging, and geoparsing tools often need databases of location names, known as gazetteers.
Human–computer information retrieval (HCIR) is the study and engineering of information retrieval techniques that bring human intelligence into the search process. It combines the fields of human-computer interaction (HCI) and information retrieval (IR) and creates systems that improve search by taking into account the human context, or through a multi-step search process that provides the opportunity for human feedback.
A concept search is an automated information retrieval method that is used to search electronically stored unstructured text for information that is conceptually similar to the information provided in a search query. In other words, the ideas expressed in the information retrieved in response to a concept search query are relevant to the ideas contained in the text of the query.
Lucene Geographic and Temporal (LGTE) is an information retrieval tool developed at Technical University of Lisbon which can be used as a search engine or as evaluation system for information retrieval techniques for research purposes. The first implementation powered by LGTE was the search engine of DIGMAP, a project co-funded by the community programme eContentplus between 2006 and 2008, which was aimed to provide services available on the web over old digitized maps from a group of partners over Europe including several National Libraries.
The knowledge acquisition bottleneck is perhaps the major impediment to solving the word sense disambiguation (WSD) problem. Unsupervised learning methods rely on knowledge about word senses, which is barely formulated in dictionaries and lexical databases. Supervised learning methods depend heavily on the existence of manually annotated examples for every word sense, a requisite that can so far be met only for a handful of words for testing purposes, as it is done in the Senseval exercises.
The Conference and Labs of the Evaluation Forum, or CLEF, is an organization promoting research in multilingual information access. Its specific functions are to maintain an underlying framework for testing information retrieval systems and to create repositories of data for researchers to use in developing comparable standards. The organization holds a conference every September in Europe since a first constituting workshop in 2000. From 1997 to 1999, TREC, the similar evaluation conference organised annually in the USA, included a track for the evaluation of Cross-Language IR for European languages. This track was coordinated jointly by NIST and by a group of European volunteers that grew over the years. At the end of 1999, a decision by some of the participants was made to transfer the activity to Europe and set it up independently. The aim was to expand coverage to a larger number of languages and to focus on a wider range of issues, including monolingual system evaluation for languages other than English. Over the years, CLEF has been supported by a number of various EU funded projects and initiatives.
SemEval is an ongoing series of evaluations of computational semantic analysis systems; it evolved from the Senseval word sense evaluation series. The evaluations are intended to explore the nature of meaning in language. While meaning is intuitive to humans, transferring those intuitions to computational analysis has proved elusive.
In linguistic morphology and information retrieval, stemming is the process of reducing inflected words to their word stem, base or root form—generally 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 1960s. Many search engines treat words with the same stem as synonyms as a kind of query expansion, a process called conflation.
Text, Speech and Dialogue (TSD) is an annual conference involving topics on natural language processing and computational linguistics. The meeting is held every September alternating in Brno and Plzeň, Czech Republic.
The following outline is provided as an overview of and topical guide to natural-language processing:
In natural language processing, entity linking, also referred to as named-entity linking (NEL), named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD) or named-entity normalization (NEN) is the task of assigning a unique identity to entities mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris". Entity linking is different from named-entity recognition (NER) in that NER identifies the occurrence of a named entity in text but it does not identify which specific entity it is.
The Center for the Evaluation of Language and Communication Technologies (CELCT) was an organisation devoted to the evaluation of language technologies, located in Povo, Trento (Italy).
