Whereas a deterministic model will always produce the same result from a given starting condition or initial state.
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Time series: types and their nature
▪️Stochastic: the property of being well described by a random probability distribution.
▪️Deterministic: system in which no randomness is involved in the development of future states.
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More from Andres Segura-Tinoco
Text Normalization (TN) are techniques in the field of #NLP that are used to prepare text, sentences, and words for further processing or analysis.
Two of the most common TN techniques are Stemming and Lemmatization. In the next thread🧵 I will briefly tell you about them.
1/5
The aim of both methods (Stemming and Lemmatization) is the same: to reduce the inflectional forms of each word/term into a common base or root.
So what is the difference between them?
2/5
Stemming: process in which terms are transformed to their root in order to reduce the size of the vocabulary. It is carried by applying word reduction rules.
Two of the most common stemming algorithms are:
▪️Porter
▪️Snowball
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Lemmatization: it performs a morphological analysis using reference dictionaries to create equivalence classes between words.
For example, for the token “eclipses”, a stemming rule would return the term “eclips“, while through lemmatization we would get the term “eclipse“.
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Finally, let me share a quick example on the use of these two NLP techniques (with spaCy and Python):
https://t.co/Qm0Fa4cGaV
5/5
Two of the most common TN techniques are Stemming and Lemmatization. In the next thread🧵 I will briefly tell you about them.
1/5
The aim of both methods (Stemming and Lemmatization) is the same: to reduce the inflectional forms of each word/term into a common base or root.
So what is the difference between them?
2/5
Stemming: process in which terms are transformed to their root in order to reduce the size of the vocabulary. It is carried by applying word reduction rules.
Two of the most common stemming algorithms are:
▪️Porter
▪️Snowball
3/5
Lemmatization: it performs a morphological analysis using reference dictionaries to create equivalence classes between words.
For example, for the token “eclipses”, a stemming rule would return the term “eclips“, while through lemmatization we would get the term “eclipse“.
4/5
Finally, let me share a quick example on the use of these two NLP techniques (with spaCy and Python):
https://t.co/Qm0Fa4cGaV
5/5
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"I lied about my basic beliefs in order to keep a prestigious job. Now that it will be zero-cost to me, I have a few things to say."
We know that elite institutions like the one Flier was in (partial) charge of rely on irrelevant status markers like private school education, whiteness, legacy, and ability to charm an old white guy at an interview.
Harvard's discriminatory policies are becoming increasingly well known, across the political spectrum (see, e.g., the recent lawsuit on discrimination against East Asian applications.)
It's refreshing to hear a senior administrator admits to personally opposing policies that attempt to remedy these basic flaws. These are flaws that harm his institution's ability to do cutting-edge research and to serve the public.
Harvard is being eclipsed by institutions that have different ideas about how to run a 21st Century institution. Stanford, for one; the UC system; the "public Ivys".
As a dean of a major academic institution, I could not have said this. But I will now. Requiring such statements in applications for appointments and promotions is an affront to academic freedom, and diminishes the true value of diversity, equity of inclusion by trivializing it. https://t.co/NfcI5VLODi
— Jeffrey Flier (@jflier) November 10, 2018
We know that elite institutions like the one Flier was in (partial) charge of rely on irrelevant status markers like private school education, whiteness, legacy, and ability to charm an old white guy at an interview.
Harvard's discriminatory policies are becoming increasingly well known, across the political spectrum (see, e.g., the recent lawsuit on discrimination against East Asian applications.)
It's refreshing to hear a senior administrator admits to personally opposing policies that attempt to remedy these basic flaws. These are flaws that harm his institution's ability to do cutting-edge research and to serve the public.
Harvard is being eclipsed by institutions that have different ideas about how to run a 21st Century institution. Stanford, for one; the UC system; the "public Ivys".
