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.
Music information retrieval (MIR) is the interdisciplinary science of retrieving information from music. Those involved in MIR may have a background in academic musicology, psychoacoustics, psychology, signal processing, informatics, machine learning, optical music recognition, computational intelligence or some combination of these.
Cross-language information retrieval (CLIR) is a subfield of information retrieval dealing with retrieving information written in a language different from the language of the user's query. The term "cross-language information retrieval" has many synonyms, of which the following are perhaps the most frequent: cross-lingual information retrieval, translingual information retrieval, multilingual information retrieval. The term "multilingual information retrieval" refers more generally both to technology for retrieval of multilingual collections and to technology which has been moved to handle material in one language to another. The term Multilingual Information Retrieval (MLIR) involves the study of systems that accept queries for information in various languages and return objects of various languages, translated into the user's language. Cross-language information retrieval refers more specifically to the use case where users formulate their information need in one language and the system retrieves relevant documents in another. To do so, most CLIR systems use various translation techniques. CLIR techniques can be classified into different categories based on different translation resources:
Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005) we can distinguish between three different perspectives of text mining: information extraction, data mining, and a knowledge discovery in databases (KDD) process. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.
Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language.
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction.
In information science and information retrieval, relevance denotes how well a retrieved document or set of documents meets the information need of the user. Relevance may include concerns such as timeliness, authority or novelty of the result.
Automatic summarization is the process of shortening a set of data computationally, to create a subset that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data.
Document classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories. This may be done "manually" or algorithmically. The intellectual classification of documents has mostly been the province of library science, while the algorithmic classification of documents is mainly in information science and computer science. The problems are overlapping, however, and there is therefore interdisciplinary research on document classification.
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.
Query expansion (QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding. In the context of search engines, query expansion involves evaluating a user's input and expanding the search query to match additional documents. Query expansion involves techniques such as:
Terminology extraction is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus.
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 Web query topic classification/categorization is a problem in information science. The task is to assign a Web search query to one or more predefined categories, based on its topics. The importance of query classification is underscored by many services provided by Web search. A direct application is to provide better search result pages for users with interests of different categories. For example, the users issuing a Web query "apple" might expect to see Web pages related to the fruit apple, or they may prefer to see products or news related to the computer company. Online advertisement services can rely on the query classification results to promote different products more accurately. Search result pages can be grouped according to the categories predicted by a query classification algorithm. However, the computation of query classification is non-trivial. Different from the document classification tasks, queries submitted by Web search users are usually short and ambiguous; also the meanings of the queries are evolving over time. Therefore, query topic classification is much more difficult than traditional document classification tasks.
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.
Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL, the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data.
Dragomir R. Radev was an American computer scientist who was a professor at Yale University, working on natural language processing and information retrieval. He also served as a University of Michigan computer science professor and Columbia University computer science adjunct professor, as well as a Member of the Advisory Board of Lawyaw.
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.
