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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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Navigating unstructured data: The rise of question answering

Question answering technologies are key to efficiently dealing with overwhelming amounts of unstructured data.

Organisations deal with vast amounts of text-based data on a daily basis. Text and unstructured data in general are involved in some of the core parts of every business: customer communication, reference documentation, and reporting, to name a few.

Natural language processing (NLP) and other emerging cognitive technologies are capable of analysing textual information in ways never before possible and are becoming widely used to enhance, scale, and automate various business processes. Examples of such processes include customer support and service, internal knowledge base search, content management, and research.

These and many other examples often require reading through large amounts of text and finding exact answers to given questions. The area of NLP that is concerned with building systems capable of automating these tasks is called machine reading comprehension, or more narrowly, question answering.

What is question answering?

Question answering systems are trained to answer user questions posed in a natural language by returning the relevant segments of the given text. For every question posed, they provide the exact locations of candidate answers.

Suppose that you need to analyse this excerpt from the Wikipedia article about Auckland:

Auckland is a metropolitan city in the North Island of New Zealand. The most populous urban area in the country, Auckland has an urban population of about 1,467,800 (June 2019). It is located in the Auckland Region, the area governed by Auckland Council, which includes outlying rural areas and the islands of the Hauraki Gulf, resulting in a total population of 1,642,800. Auckland is a diverse, multicultural and cosmopolitan city, home to the largest Polynesian population in the world. The Māori-language name for Auckland is Tāmaki Makaurau, meaning "Tāmaki desired by many", in reference to the desirability of its natural resources and geography.

Below are the questions you may ask about this passage, with the answers generated automatically by a question answering system:

QuestionsAnswers
Where is Auckland located?It is located in the Auckland Region, the area governed by Auckland Council, ...
How many people live in Auckland?... Auckland has an urban population of about 1,467,800 (June 2019).
What does Tāmaki Makaurau mean?The Māori-language name for Auckland is Tāmaki Makaurau, meaning "Tāmaki desired by many", ...

In the examples above, the exact answers returned by the question answering implementation are highlighted in bold.

Question answering vs keyword-based search

While both question answering and keyword matching systems assist in navigating text content, there are some key differences between the two technologies.

First of all, unlike keyword-based search engines, question answering systems take questions posed in a natural language as an input rather than a set of keywords. For example, question answering systems will "understand" and find the answer to "How many people live in Auckland?", while keyword-based search engines will only expect keywords.

Secondly and more importantly, rather than simply finding the occurrences of matched keywords in the given documents (as standard search engines do), question answering systems look for segments that answer the input questions. For the "How many people live in Auckland?" question, our question answering system did not highlight the occurrences of "people" or "Auckland" but rather highlight the actual statistic mentioned in the article.

Question answering in practice

Question answering and other NLP techniques are used across a range of industries as diverse as healthcare, law, and retail. The recent advances in machine reading comprehension have resulted in these technologies being used to power countless intelligent applications for a wide variety of business processes within these industries.

For example, in the field of law, practitioners can easily get answers about legal cases by posing questions to intelligent assistants which are capable of analysing hundreds of pages of text and legal documentation in a matter of seconds.

The bottom line

Question answering and natural language processing in general help organisations efficiently navigate their data and allow them to improve operations, risk management, and other business processes. Used across a spectrum of business domains, question answering helps deal with overwhelming amounts of text and powers multiple intelligent applications that modernise the way businesses handle their data.

Last updated on 25 Aug 2020 by Anton Vasetenkov
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See also
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.
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.
Let's explore the Nobel Prize dataset
An overview of the official Nobel Prize Linked Data dataset with some example SPARQL queries.
Why federation is a game-changing feature of SPARQL
SPARQL federation is an incredibly useful feature for querying distributed RDF graphs.
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