Related Research Articles
Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries 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.
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.
Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text. A matrix containing word counts per document is constructed from a large piece of text and a mathematical technique called singular value decomposition (SVD) is used to reduce the number of rows while preserving the similarity structure among columns. Documents are then compared by cosine similarity between any two columns. Values close to 1 represent very similar documents while values close to 0 represent very dissimilar documents.
Document retrieval is defined as the matching of some stated user query against a set of free-text records. These records could be any type of mainly unstructured text, such as newspaper articles, real estate records or paragraphs in a manual. User queries can range from multi-sentence full descriptions of an information need to a few words.
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.
In computer science, an inverted index is a database index storing a mapping from content, such as words or numbers, to its locations in a table, or in a document or a set of documents. The purpose of an inverted index is to allow fast full-text searches, at a cost of increased processing when a document is added to the database. The inverted file may be the database file itself, rather than its index. It is the most popular data structure used in document retrieval systems, used on a large scale for example in search engines. Additionally, several significant general-purpose mainframe-based database management systems have used inverted list architectures, including ADABAS, DATACOM/DB, and Model 204.
Exploratory search is a specialization of information exploration which represents the activities carried out by searchers who are:
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:
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.
Discounted cumulative gain (DCG) is a measure of ranking quality in information retrieval. It is often normalized so that it is comparable across queries, giving Normalized DCG (nDCG or NDCG). NDCG is often used to measure effectiveness of search engine algorithms and related applications. Using a graded relevance scale of documents in a search-engine result set, DCG sums the usefulness, or gain, of the results discounted by their position in the result list. NDCG is DCG normalized by the maximum possible DCG of the result set when ranked from highest to lowest gain, thus adjusting for the different numbers of relevant results for different queries.
Ranking of query is one of the fundamental problems in information retrieval (IR), the scientific/engineering discipline behind search engines. Given a query q and a collection D of documents that match the query, the problem is to rank, that is, sort, the documents in D according to some criterion so that the "best" results appear early in the result list displayed to the user. Ranking in terms of information retrieval is an important concept in computer science and is used in many different applications such as search engine queries and recommender systems. A majority of search engines use ranking algorithms to provide users with accurate and relevant results.
Expertise finding is the use of tools for finding and assessing individual expertise. In the recruitment industry, expertise finding is the problem of searching for employable candidates with certain required skills set. In other words, it is the challenge of linking humans to expertise areas, and as such is a sub-problem of expertise retrieval.
XML retrieval, or XML information retrieval, is the content-based retrieval of documents structured with XML. As such it is used for computing relevance of XML documents.
Legal information retrieval is the science of information retrieval applied to legal text, including legislation, case law, and scholarly works. Accurate legal information retrieval is important to provide access to the law to laymen and legal professionals. Its importance has increased because of the vast and quickly increasing amount of legal documents available through electronic means. Legal information retrieval is a part of the growing field of legal informatics.
Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data may, for example, consist of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data.
Collaborative information seeking (CIS) is a field of research that involves studying situations, motivations, and methods for people working in collaborative groups for information seeking projects, as well as building systems for supporting such activities. Such projects often involve information searching or information retrieval (IR), information gathering, and information sharing. Beyond that, CIS can extend to collaborative information synthesis and collaborative sense-making.
In natural language processing, Entity Linking, also referred to as named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD), named-entity normalization (NEN), or Concept Recognition, 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 main idea is to first identify "Paris" and "France" as named entities, and then 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" and "France" to the french country.
Evaluation measures for an information retrieval (IR) system assess how well an index, search engine, or database returns results from a collection of resources that satisfy a user's query. They are therefore fundamental to the success of information systems and digital platforms.
References
- Azzopardi, L. & Vinay, V. (2008). "Retrievability: an evaluation measure for higher order information access tasks". Proceedings of the 17th ACM conference on Information and knowledge management. CIKM '08. Napa Valley, California, USA: ACM. pp. 561–570. doi:10.1145/1458082.1458157. ISBN 9781595939913. S2CID 8705350.
- Azzopardi, L. & Vinay, V. (2008). "Accessibility in information retrieval". Proceedings of the IR research, 30th European conference on Advances in information retrieval. ECIR '08. Glasgow, UK: Springer. pp. 482–489. ISBN 9783540786450 . Retrieved 7 Dec 2016.