Annotation

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An annotation is extra information associated with a particular point in a document or other piece of information. It can be a note that includes a comment or explanation. [1] Annotations are sometimes presented in the margin of book pages. For annotations of different digital media, see web annotation and text annotation.

Contents

Literature, grammar and educational purposes

Practising Visually

Annotation Practices are highlighting a phrase or sentence and including a comment, circling a word that needs defining, posing a question when something is not fully understood and writing a short summary of a key section. [2] It also invites students to "(re)construct a history through material engagement and exciting DIY (Do-It-Yourself) annotation practices." [3] Annotation practices that are available today offer a remarkable set of tools for students to begin to work, and in a more collaborative, connected way than has been previously possible. [4]

Text and Film Annotation

Text and Film Annotation is a technique that involves using comments, text within a film. Analyzing videos is an undertaking that is never entirely free of preconceived notions, and the first step for researchers is to find their bearings within the field of possible research approaches and thus reflect on their own basic assumptions. [5] Annotations can take part within the video, and can be used when the data video is recorded. It is being used as a tool in text and film to write one's thoughts and emotion into the markings. [2] In any number of steps of analysis, it can also be supplemented with more annotations. Anthropologists Clifford Geertz calls it a "thick description." This can give a sense of how useful annotation is, especially by adding a description of how it can be implemented in film. [5]

Medieval Marginalia

Marginalia refers to writing or decoration in the margins of a manuscript. Medieval marginalia is so well known that amusing or disconcerting instances of it are fodder for viral aggregators such as Buzzfeed and Brainpickings, and the fascination with other readers’ reading is manifest in sites such as Melville's Marginalia Online or Harvard's online exhibit of marginalia from six personal libraries. [4] It can also be a part of other websites such as Pinterest, or even meme generators and GIF tools.

Textual scholarship

Textual scholarship is a discipline that often uses the technique of annotation to describe or add additional historical context to texts and physical documents to make it easier to understand. [6]

Student uses

Students often highlight passages in books in order to actively engage with the text. Students can use annotations to refer back to key phrases easily, or add marginalia to aid studying and finding connections between the text and prior knowledge or running themes. [7]

Annotated bibliographies add commentary on the relevance or quality of each source, in addition to the usual bibliographic information that merely identifies the source.

Students use Annotation not only for academic purposes, but interpreting their own thoughts, feelings, and emotions. [2] Sites such as Scalar and Omeka are sites that students use. There are multiple genres with Annotation such as math, film, linguists, and literary theory which students find it most helpful to use. Most students reported the annotation process as helpful for improving overall writing ability, grammar, and academic vocabulary knowledge.

Mathematical expression annotation

Mathematical expressions (symbols and formulae) can be annotated with their natural language meaning. This is essential for disambiguation, since symbols may have different meanings (e.g., "E" can be "energy" or "expectation value", etc.). [8] [9] The annotation process can be facilitated and accelerated through recommendation, e.g., using the "AnnoMathTeX" system that is hosted by Wikimedia. [10] [11] [12]

Learning and instruction

From a cognitive perspective, annotation has an important role in learning and instruction. As part of guided noticing it involves highlighting, naming or labelling and commenting aspects of visual representations to help focus learners' attention on specific visual aspects. In other words, it means the assignment of typological representations (culturally meaningful categories), to topological representations (e.g. images). [13] This is especially important when experts, such as medical doctors, interpret visualizations in detail and explain their interpretations to others, for example by means of digital technology. [14] Here, annotation can be a way to establish common ground between interactants with different levels of knowledge. [15] The value of annotation has been empirically confirmed, for example, in a study which shows that in computer-based teleconsultations the integration of image annotation and speech leads to significantly improved knowledge exchange compared with the use of images and speech without annotation. [16]

On YouTube

Annotations were removed on January 15, 2019, from YouTube after around a decade of service. [17] They had allowed users to provide information that popped up during videos, but YouTube indicated they did not work well on small mobile screens, and were being abused.

Software and engineering

Text documents

Markup languages like XML and HTML annotate text in a way that is syntactically distinguishable from that text. They can be used to add information about the desired visual presentation, or machine-readable semantic information, as in the semantic web. [18]

Tabular data

This includes CSV and XLS. The process of assigning semantic annotations to tabular data is referred to as semantic labelling. Semantic Labelling is the process of assigning annotations from ontologies to tabular data. [19] [20] [21] [22] This process is also referred to as semantic annotation. [23] [22] Semantic Labelling is often done in a (semi-)automatic fashion. Semantic Labelling techniques work on entity columns, [22] numeric columns, [19] [21] [24] [25] coordinates, [26] and more. [26] [25]

Semantic Labelling Techniques

There are several semantic labelling types which utilises machine learning techniques. These techniques can be categorised following the work of Flach [27] [28] as follows: geometric (using lines and planes, such as Support-vector machine, Linear regression), probabilistic (e.g., Conditional random field), logical (e.g., Decision tree learning), and Non-ML techniques (e.g., balancing coverage and specificity [22] ). Note that the geometric, probabilistic, and logical machine learning models are not mutually exclusive. [27]

Geometric Techniques

Pham et al. [29] use Jaccard index and TF-IDF similarity for textual data and Kolmogorov–Smirnov test for the numeric ones. Alobaid and Corcho [21] use fuzzy clustering (c-means [30] [31] ) to label numeric columns.

Probabilistic Techniques

Limaye et al. [32] uses TF-IDF similarity and graphical models. They also use support-vector machine to compute the weights. Venetis et al. [33] construct an isA database which consists of the pairs (instance, class) and then compute maximum likelihood using these pairs. Alobaid and Corcho [34] approximated the q-q plot for predicting the properties of numeric columns.

Logical Techniques

Syed et al. [35] built Wikitology, which is "a hybrid knowledge base of structured and unstructured information extracted from Wikipedia augmented by RDF data from DBpedia and other Linked Data resources." [35] For the Wikitology index, they use PageRank for Entity linking, which is one of the tasks often used in semantic labelling. Since they were not able to query Google for all Wikipedia articles to get the PageRank, they used Decision tree to approximate it. [35]

Non-ML techniques

Alobaid and Corcho [22] presented an approach to annotate entity columns. The technique starts by annotating the cells in the entity column with the entities from the reference knowledge graph (e.g., DBpedia). The classes are then gathered and each one of them is scored based on several formulas they presented taking into account the frequency of each class and their depth according to the subClass hierarchy. [36]

Semantic Labelling Common Tasks

Here are some of the common semantic labelling tasks presented in the literature:

Entity Linking and Disambiguation

This is the most common task in semantic labelling. Given a text of a cell and a data source, the approach predicts the entity and link it to the one identified in the given data source. For example, if the input to the approach were the text "Richard Feynman" and a URL to the SPARQL endpoint of DBpedia, the approach would return "http://dbpedia.org/resource/Richard_Feynman", which is the entity from DBpedia. Some approaches use exact match. [22] while others use similarity metrics such as Cosine similarity [32]

Subject Column Identification

The subject column of a table is the column that contain the main subjects/entities in the table. [19] [28] [33] [37] [38] Some approaches expects the subject column as an input [22] while others predict the subject column such as TableMiner+. [38]

Column Data-Type Detection

Columns types are divided differently by different approaches. [28] Some divide them into strings/text and numbers [21] [29] [39] [25] while others divide them further [28] (e.g., Number Typology, [19] Date, [35] [33] coordinates [40] ).

Relation Prediction

The relation between Madrid and Spain is "capitalOf". [41] Such relations can easily be found in ontologies, such as DBpedia. Venetis et al. [33] use TextRunner [42] to extract the relation between two columns. Syed et al. [35] use the relation between the entities of the two columns and the most frequent relation is selected.

Gold Standards

T2D [43] is the most common gold standard for semantic labelling. Two versions exists of T2D: T2Dv1 (sometimes are referred to T2D as well) and T2Dv2. [43] Another known benchmarks are published with the SemTab Challenge. [44]

Source control

The "annotate" function (also known as "blame" or "praise") used in source control systems such as Git, Team Foundation Server and Subversion determines who committed changes to the source code into the repository. This outputs a copy of the source code where each line is annotated with the name of the last contributor to edit that line (and possibly a revision number). This can help establish blame in the event a change caused a malfunction, or identify the author of brilliant code.

Java annotations

A special case is the Java programming language, where annotations can be used as a special form of syntactic metadata in the source code. [45] Classes, methods, variables, parameters and packages may be annotated. The annotations can be embedded in class files generated by the compiler and may be retained by the Java virtual machine and thus influence the run-time behaviour of an application. It is possible to create meta-annotations out of the existing ones in Java. [46]

Image annotation

Automatic image annotation is used to classify images for image retrieval systems. [47]

Computational biology

Since the 1980s, molecular biology and bioinformatics have created the need for DNA annotation. DNA annotation or genome annotation is the process of identifying the locations of genes and all of the coding regions in a genome and determining what those genes do. An annotation (irrespective of the context) is a note added by way of explanation or commentary. Once a genome is sequenced, it needs to be annotated to make sense of it. [48]

Digital imaging

In the digital imaging community the term annotation is commonly used for visible metadata superimposed on an image without changing the underlying master image, such as sticky notes, virtual laser pointers, circles, arrows, and black-outs (cf. redaction). [49]

In the medical imaging community, an annotation is often referred to as a region of interest and is encoded in DICOM format.

Other uses

Law

In the United States, legal publishers such as Thomson West and Lexis Nexis publish annotated versions of statutes, providing information about court cases that have interpreted the statutes. Both the federal United States Code and state statutes are subject to interpretation by the courts, and the annotated statutes are valuable tools in legal research. [50]

Linguistics

One purpose of annotation is to transform the data into a form suitable for computer-aided analysis. Prior to annotation, an annotation scheme is defined that typically consists of tags. During tagging, transcriptionists manually add tags into transcripts where required linguistical features are identified in an annotation editor. The annotation scheme ensures that the tags are added consistently across the data set and allows for verification of previously tagged data. [51] Aside from tags, more complex forms of linguistic annotation include the annotation of phrases and relations, e.g., in treebanks. Many different forms of linguistic annotation have been developed, as well as different formats and tools for creating and managing linguistic annotations, as described, for example, in the Linguistic Annotation Wiki. [52]

See also

Related Research Articles

Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval. It is primarily concerned with giving computers the ability to support and manipulate human language. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. To this end, natural language processing often borrows ideas from theoretical linguistics. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

<span class="mw-page-title-main">Semantic Web</span> Extension of the Web to facilitate data exchange

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

<span class="mw-page-title-main">Table (information)</span> Arrangement of information or data, typically in rows and columns

A table is an arrangement of information or data, typically in rows and columns, or possibly in a more complex structure. Tables are widely used in communication, research, and data analysis. Tables appear in print media, handwritten notes, computer software, architectural ornamentation, traffic signs, and many other places. The precise conventions and terminology for describing tables vary depending on the context. Further, tables differ significantly in variety, structure, flexibility, notation, representation and use. Information or data conveyed in table form is said to be in tabular format. In books and technical articles, tables are typically presented apart from the main text in numbered and captioned floating blocks.

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. Typically, this involves 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.

Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature. The term semantic similarity is often confused with semantic relatedness. Semantic relatedness includes any relation between two terms, while semantic similarity only includes "is a" relations. For example, "car" is similar to "bus", but is also related to "road" and "driving".

<span class="mw-page-title-main">Treebank</span>

In linguistics, a treebank is a parsed text corpus that annotates syntactic or semantic sentence structure. The construction of parsed corpora in the early 1990s revolutionized computational linguistics, which benefitted from large-scale empirical data.

Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly.

Ontotext is a software company with offices in Europe and USA. It is the semantic technology branch of Sirma Group. Its main domain of activity is the development of software based on the Semantic Web languages and standards, in particular RDF, OWL and SPARQL. Ontotext is best known for the Ontotext GraphDB semantic graph database engine. Another major business line is the development of enterprise knowledge management and analytics systems that involve big knowledge graphs. Those systems are developed on top of the Ontotext Platform that builds on top of GraphDB capabilities for text mining using big knowledge graphs.

<span class="mw-page-title-main">DBpedia</span> Online database project

DBpedia is a project aiming to extract structured content from the information created in the Wikipedia project. This structured information is made available on the World Wide Web using OpenLink Virtuoso. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datasets.

<span class="mw-page-title-main">Text annotation</span> Adding a note or gloss to a text

Text annotation is the practice and the result of adding a note or gloss to a text, which may include highlights or underlining, comments, footnotes, tags, and links. Text annotations can include notes written for a reader's private purposes, as well as shared annotations written for the purposes of collaborative writing and editing, commentary, or social reading and sharing. In some fields, text annotation is comparable to metadata insofar as it is added post hoc and provides information about a text without fundamentally altering that original text. Text annotations are sometimes referred to as marginalia, though some reserve this term specifically for hand-written notes made in the margins of books or manuscripts. Annotations have been found to be useful and help to develop knowledge of English literature.

The International Semantic Web Conference (ISWC) is a series of academic conferences and the premier international forum for the Semantic Web, Linked Data and Knowledge Graph Community. Here, scientists, industry specialists, and practitioners meet to discuss the future of practical, scalable, user-friendly, and game changing solutions. Its proceedings are published in the Lecture Notes in Computer Science by Springer-Verlag.

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.

<span class="mw-page-title-main">Entity linking</span> Concept in 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.

<span class="mw-page-title-main">UMBEL</span>

UMBEL is a logically organized knowledge graph of 34,000 concepts and entity types that can be used in information science for relating information from disparate sources to one another. It was retired at the end of 2019. UMBEL was first released in July 2008. Version 1.00 was released in February 2011. Its current release is version 1.50.

In natural language processing, linguistics, and neighboring fields, Linguistic Linked Open Data (LLOD) describes a method and an interdisciplinary community concerned with creating, sharing, and (re-)using language resources in accordance with Linked Data principles. The Linguistic Linked Open Data Cloud was conceived and is being maintained by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation, but has been a point of focal activity for several W3C community groups, research projects, and infrastructure efforts since then.

A semantic triple, or RDF triple or simply triple, is the atomic data entity in the Resource Description Framework (RDF) data model. As its name indicates, a triple is a sequence of three entities that codifies a statement about semantic data in the form of subject–predicate–object expressions.

Drama annotation is the process of annotating the metadata of a drama. Given a drama expressed in some medium, the process of metadata annotation identifies what are the elements that characterize the drama and annotates such elements in some metadata format. For example, in the sentence "Laertes and Polonius warn Ophelia to stay away from Hamlet." from the text Hamlet, the word "Laertes", which refers to a drama element, namely a character, will be annotated as "Char", taken from some set of metadata. This article addresses the drama annotation projects, with the sets of metadata and annotations proposed in the scientific literature, based markup languages and ontologies.

In linguistics and language technology, a language resource is a "[composition] of linguistic material used in the construction, improvement and/or evaluation of language processing applications, (...) in language and language-mediated research studies and applications."

<span class="mw-page-title-main">Knowledge graph</span> Type of knowledge base

In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the semantics or relationships underlying these entities.

Table extraction is the process of recognizing and separating a table from a large document, possibly also recognizing individual rows, columns or elements. It may be regarded as a special form of information extraction.

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