Explore natural language processing
Text Analytics service - answeris a cloud-based service that provides advanced natural
language processing over raw text for sentiment analysis, key phrase extraction, named
entity recognition, and language detection.
Text Analytics - answers a process where an artificial intelligence (AI) algorithm, running
on a computer, evaluates these same attributes in text, to determine specific insights. A
person will typically rely on their own experiences and knowledge to achieve the
insights. A computer must be provided with similar knowledge to be able to perform the
task. There are some commonly used techniques that can be used to build software to
analyze text
+ Statistical analysis of terms used in the text. For example, removing common "stop
words" (words like "the" or "a", which reveal little semantic information about the text),
and performing frequency analysis of the remaining words (counting how often each
word appears) can provide clues about the main subject of the text.
+ Extending frequency analysis to multi-term phrases, commonly known as N-grams (a
two-word phrase is a bi-gram, a three-word phrase is a tri-gram, and so on).
+ Applying stemming or lemmatization algorithms to normalize words before counting
them - for example, so that words like "power", "powered", and "powerful" are
interpreted as being the same word.
+ Applying linguistic structure rules to analyze sentences - for example, breaking down
sentences into tree-like structures such as a noun phrase, which itself contains nouns,
verbs, adjectives, and so on.
+ Encoding words or terms as numeric features that can be used to train a machine
learning model. For example, to classify a text document based on the terms it contains.
This technique is often used to perform sentiment analysis, in which a document is
classified as positive or negative.
+ Creating vectorized models that capture semantic relationships between words by
assigning them to locations in n-dimensional space. This modeling technique might, for
example, assign values to the words "flower" and "plant" that locate them clo
Speech recognition - answer- the ability to detect and interpret spoken input.
is concerned with taking the spoken word and converting it into data that can be
processed - often by transcribing it into a text representation. The spoken words can be
in the form of a recorded voice in an audio file, or live audio from a microphone. Speech
patterns are analyzed in the audio to determine recognizable patterns that are mapped
to words. To accomplish this feat, the software typically uses multiple types of model,
including:
Text Analytics service - answeris a cloud-based service that provides advanced natural
language processing over raw text for sentiment analysis, key phrase extraction, named
entity recognition, and language detection.
Text Analytics - answers a process where an artificial intelligence (AI) algorithm, running
on a computer, evaluates these same attributes in text, to determine specific insights. A
person will typically rely on their own experiences and knowledge to achieve the
insights. A computer must be provided with similar knowledge to be able to perform the
task. There are some commonly used techniques that can be used to build software to
analyze text
+ Statistical analysis of terms used in the text. For example, removing common "stop
words" (words like "the" or "a", which reveal little semantic information about the text),
and performing frequency analysis of the remaining words (counting how often each
word appears) can provide clues about the main subject of the text.
+ Extending frequency analysis to multi-term phrases, commonly known as N-grams (a
two-word phrase is a bi-gram, a three-word phrase is a tri-gram, and so on).
+ Applying stemming or lemmatization algorithms to normalize words before counting
them - for example, so that words like "power", "powered", and "powerful" are
interpreted as being the same word.
+ Applying linguistic structure rules to analyze sentences - for example, breaking down
sentences into tree-like structures such as a noun phrase, which itself contains nouns,
verbs, adjectives, and so on.
+ Encoding words or terms as numeric features that can be used to train a machine
learning model. For example, to classify a text document based on the terms it contains.
This technique is often used to perform sentiment analysis, in which a document is
classified as positive or negative.
+ Creating vectorized models that capture semantic relationships between words by
assigning them to locations in n-dimensional space. This modeling technique might, for
example, assign values to the words "flower" and "plant" that locate them clo
Speech recognition - answer- the ability to detect and interpret spoken input.
is concerned with taking the spoken word and converting it into data that can be
processed - often by transcribing it into a text representation. The spoken words can be
in the form of a recorded voice in an audio file, or live audio from a microphone. Speech
patterns are analyzed in the audio to determine recognizable patterns that are mapped
to words. To accomplish this feat, the software typically uses multiple types of model,
including:
