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Sequence logos visualize protein and nucleic acid motifs and patterns identified through multiple sequence alignment. They are commonly used widely to represent transcription factor binding sites and other conserved DNA and RNA sequences. Protein sequence logos are also useful for illustrating various biological properties of proteins. Create a sequence logo with Sequence Logo. Paste your multiple sequence alignment and the sequence logo is generated automatically. Use the sequence logo maker to easily create vector sequence logo graphs. Please refer to the Sequence Logo manual for the sequence logo parameters and configuration. Sequence Logo supports multiple color schemes and download formats.

Sequence Logo is a web-based sequence logo generator. Sequence Logo generates sequence logo diagrams for proteins and nucleic acids. Sequence logos represent patterns found within multiple sequence alignments. They consist of stacks of letters, each representing a position in the sequence alignment. Sequence Logo analyzes the sequence data inside the user's web browser and does not store or transmit the alignment data via servers.

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Словообразовательный словарь «Морфема» дает представление о морфемной структуре слов русского языка и слов современной лексики. Для словообразовательного анализа представлены наиболее употребительные слова современного русского языка, их производные и словоформы. Словарь предназначен школьникам, студентам и преподавателям. Статья разбора слова «сладкоежка» по составу показывает, что это слово имеет два корня, соединительную гласную, суффикс и окончание. На странице также приведены слова, содержащие те же морфемы. Словарь «Морфема» включает в себя не только те слова, состав которых анализируется в процессе изучения предмета, но и множество других слов современного русского языка. Словарь адресован всем, кто хочет лучше понять структуру русского языка.

Разбор слова "кормушка" по составу.

Разбор слова "светить" по составу.

Разбор слова "сбоку" по составу.

Разбор слова "шиповник" по составу.

Разбор слова "народ" по составу.

Разбор слова "впервые" по составу.

Разбор слова "свежесть" по составу.

Разбор слова "издалека" по составу.

Разбор слова "лесной" по составу.

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Harnessing the power of the Oxford English Dictionary for linguistic research and NLP applications

How the OED Text Annotator may help bring text mining and natural language processing technologies to the next level.

Linguistic research and automated text analysis often involve breaking sentences apart into tokens and identifying the base form and meaning of each individual element, with the resulting "interpretative" data being either used directly or fed into various kinds of downstream processing tools and algorithms. The tokenisation algorithms used to perform this task draw on carefully crafted linguistic datasets, the quality of which often predicts the usefulness of the produced annotations.

The Oxford English Dictionary, or OED, is one of the largest dictionaries ever compiled, which makes it one of the most valuable and comprehensive linguistic datasets for the English language. The definitive record of the language published by Oxford University Press, it features about 600000 words, 3 million quotations, and over 1000 years of English.

The OED Text Annotator is the first and only publicly available tool for linguistic annotation based on the OED. Built by Oxford Languages, the system performs performs tokenisation, part-of-speech tagging, lemmatisation on a given text and links each word to its corresponding OED lexeme through sense disambiguation. In the publicly available version of the application, the word origin and usage data from the resulting annotations is picked up by another tool called the OED Text Visualizer which displays it in an interactive visual format.

So what does it all mean?

The OED Text Annotator mobilises the value and richness of the OED dataset for linguistic analysis and NLP applications. It may therefore both lead to important discoveries in all areas of research as well as help power more advanced and robust NLP systems supporting various industrial applications.

Last updated on 11 Oct 2020 by Anton Vasetenkov
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See also
Towards more linked lexicographical data: Lexemes on Wikidata
A glimpse into the meaning and other properties of words described with structured and linked data.
Document understanding: Modern techniques and real-world applications
How document understanding helps bring order to unstructured data.
Navigating unstructured data: The rise of question answering
Question answering technologies are key to efficiently dealing with overwhelming amounts of unstructured data.
A beginner's guide to graph embeddings
Understanding what graph embeddings are and why they are important for graph analytics.
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