What is the meaning of the word "arbitrary" in English?

arbitrary

[ ahr-bi-trer-ee ]

See synonyms for: arbitrary / arbitrarily / arbitrariness on Thesaurus.com

adjective

subject to individual will or judgment without restriction; contingent solely upon one's discretion:an arbitrary decision.

decided by a judge or arbiter rather than by a law or statute.

having unlimited power; uncontrolled or unrestricted by law; despotic; tyrannical:an arbitrary government.

based on whim or personal preference, without reason or pattern; random:This is an unusual encyclopedia, arranged by topics in a more or less arbitrary order.

Mathematics. undetermined; not assigned a specific value:an arbitrary constant.

noun, plural ar·bi·trar·ies.

arbitraries, Printing. (in Britain) peculiar (def. 9).

Research has shown that emoji are often misunderstood. In some cases, this misunderstanding is related to how the actual emoji design is interpreted by the viewer;[91] in other cases, the emoji that was sent is not shown in the same way on the receiving side.[92]

The first issue relates to the cultural or contextual interpretation of the emoji. When the author picks an emoji, they think about it in a certain way, but the same character may not trigger the same thoughts in the mind of the receiver[93] (see also Models of communication).

For example, people in China have developed a system for using emoji subversively, so that a smiley face could be sent to convey a despising, mocking, and even obnoxious attitude, as the orbicularis oculi (the muscle near that upper eye corner) on the face of the emoji does not move, and the orbicularis oris (the one near the mouth) tightens, which is believed to be a sign of suppressing a smile.[94]

The second problem relates to technology and branding. When an author of a message picks an emoji from a list, it is normally encoded in a non-graphical manner during the transmission, and if the author and the reader do not use the same software or operating system for their devices, the reader's device may visualize the same emoji in a different way. Small changes to a character's look may completely alter its perceived meaning with the receiver. As an example, in April 2020, British actress and presenter Jameela Jamil posted a tweet from her iPhone using the Face with Hand Over Mouth emoji (🤭) as part of a comment on people shopping for food during the COVID-19 pandemic. On Apple's iOS, the emoji expression is neutral and pensive, but on other platforms the emoji shows as a giggling face. Many fans were initially upset thinking that she, as a well off celebrity, was mocking poor people, but this was not her intended meaning.[95]

https://en.wikipedia.org/wiki/Emoji

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In natural language processing, word sense disambiguation (WSD) is the problem of determining which "sense" (meaning) of a word is activated by the use of the word in a particular context, a process which appears to be largely unconscious in people. WSD is a natural classification problem: Given a word and its possible senses, as defined by a dictionary, classify an occurrence of the word in context into one or more of its sense classes. The features of the context (such as neighboring words) provide the evidence for classification.

A famous example is to determine the sense of pen in the following passage (Bar-Hillel 1960):

Little John was looking for his toy box. Finally he found it. The box was in the pen. John was very happy. WordNet lists five senses for the word pen:

pen — a writing implement with a point from which ink flows. pen — an enclosure for confining livestock. playpen, pen — a portable enclosure in which babies may be left to play. penitentiary, pen — a correctional institution for those convicted of major crimes. pen — female swan. Research has progressed steadily to the point where WSD systems achieve consistent levels of accuracy on a variety of word types and ambiguities. A rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date.

Current accuracy is difficult to state without a host of caveats. On English, accuracy at the coarse-grained (homograph) level is routinely above 90%, with some methods on particular homographs achieving over 96%. On finer-grained sense distinctions, top accuracies from 59.1% to 69.0% have been reported in recent evaluation exercises (SemEval-2007, Senseval-2), where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively.

http://www.scholarpedia.org/article/Word_sense_disambiguation

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In statistics and information theory, a maximum entropy probability distribution has entropy that is at least as great as that of all other members of a specified class of probability distributions. According to the principle of maximum entropy, if nothing is known about a distribution except that it belongs to a certain class (usually defined in terms of specified properties or measures), then the distribution with the largest entropy should be chosen as the least-informative default. The motivation is twofold: first, maximizing entropy minimizes the amount of prior information built into the distribution; second, many physical systems tend to move towards maximal entropy configurations over time. . https://en.wikipedia.org/wiki/Maximum_entropy_probability_distribution

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Text representation is one of the fundamental problems in text mining and Information Retrieval (IR). It aims to numerically represent the unstructured text documents to make them mathematically computable. For a given set of text documents D = {di, i=1, 2,...,n}, where each di stands for a document, the problem of text representation is to represent each di of D as a point si in a numerical space S, where the distance/similarity between each pair of points in space S is well defined.
https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-39940-9_420
A phrase is a group of words that express a concept and is used as a unit within a sentence. Eight common types of phrases are: noun, verb, gerund, infinitive, appositive, participial, prepositional, and absolute.
https://examples.yourdictionary.com/phrase-examples.html

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The Google Ngram Viewer or Google Books Ngram Viewer is an online search engine that charts the frequencies of any set of search strings using a yearly count of n-grams found in sources printed between 1500 and 2019[1][2][3][4][5] in Google's text corpora in English, Chinese (simplified), French, German, Hebrew, Italian, Russian, or Spanish.[2][6] There are also some specialized English corpora, such as American English, British English, and English Fiction.[7] . The program can search for a word or a phrase, including misspellings or gibberish.[6] The n-grams are matched with the text within the selected corpus, optionally using case-sensitive spelling (which compares the exact use of uppercase letters),[8] and, if found in 40 or more books, are then displayed as a graph.[9] . The Google Ngram Viewer supports searches for parts of speech and wildcards.[7] It is routinely used in research.[10][11] . https://en.wikipedia.org/wiki/Google_Ngram_Viewer

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An emoji (/ɪˈmoʊdʒiː/ i-MOH-jee; plural emoji or emojis[1]) is a pictogram, logogram, ideogram or smiley used in electronic messages and web pages. The primary function of emojis is to fill in emotional cues otherwise missing from typed conversation.[2] Some examples of emojis are 😂, 😃, 🧘🏻‍♂️, 🌍, 🍞, 🚗, 📞, 😶‍🌫️, 🎉, ❤️, 🍆, and 🏁. Emojis exist in various genres, including facial expressions, common objects, places and types of weather, and animals. They are much like emoticons, but emojis are pictures rather than typographic approximations; the term "emoji" in the strict sense refers to such pictures which can be represented as encoded characters, but it is sometimes applied to messaging stickers by extension.[3] Originally meaning pictograph, the word emoji comes from Japanese e (絵, 'picture') + moji (文字, 'character'); the resemblance to the English words emotion and emoticon is purely coincidental.[4] The ISO 15924 script code for emoji is Zsye. . Originating on Japanese mobile phones in 1997, emojis became increasingly popular worldwide in the 2010s after being added to several mobile operating systems.[5][6][7] They are now considered to be a large part of popular culture in the West and around the world.[8][9] In 2015, Oxford Dictionaries named the Face with Tears of Joy emoji (😂) the word of the year.[10][11] . https://en.wikipedia.org/wiki/Emoji

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Speech and Language Processing

Speech and Language Processing (3rd ed. draft)
Dan Jurafsky and James H. Martin

Here's our September 21, 2021 draft! This is just an update draft, fixing bugs and filling in various missing sections (more on transformers, including for MT, various updated algorithms, like for dependency parsers, etc.). Expect another release later this fall with drafts of some of the 3 missing chapters.
We are really grateful to all of you for finding bugs and offering great suggestions!

Individual chapters are below; here is a single pdf of all the chapters in the Sep 21, 2021 draft of the book-so-far

As always, typos and comments very welcome (just email slp3edbugs@gmail.com and let us know the date on the draft)!
(Due to reorganizing, still expect some missing latex cross-references throughout the pdfs, don't bother reporting those missing ref/typos.)

Feel free to use the draft slides in your classes.

When will the whole book be finished?
Don't ask. But we're still shooting for before the end of 2021 for the 3 remaining chapters (Intro, Contextual Embeddings, Semantic Parsing) + random missing sections, but we'll see, and then the publishing process of course takes time.

And if you need last year's draft chapters, they are here.

	Chapter	Slides	Relation to 2nd ed.
1:	Introduction		[Ch. 1 in 2nd ed.]
2:	Regular Expressions, Text Normalization, Edit Distance	2: Text Processing [pptx] [pdf] 2: Edit Distance [pptx] [pdf]	[Ch. 2 and parts of Ch. 3 in 2nd ed.]
3:	N-gram Language Models	3: N-grams [pptx] [pdf]	[Ch. 4 in 2nd ed.]
4:	Naive Bayes and Sentiment Classification	4: Naive Bayes + Sentiment [pptx] [pdf]	[new in this edition]
5:	Logistic Regression	5: LR [pptx] [pdf]	[new in this edition]
6:	Vector Semantics and Embeddings	6: Vector Semantics [pptx] [pdf]	[new in this edition]
7:	Neural Networks and Neural Language Models	7: Neural Networks [pptx] [pdf]	[new in this edition]
8:	Sequence Labeling for Parts of Speech and Named Entities	8: POS/NER Intro only [pptx] [pdf]	[expanded from Ch. 5 in 2nd ed.]
9:	Deep Learning Architectures for Sequence Processing
10:	Machine Translation		[newly written for this edition, earlier MT was Ch. 25 in 2nd ed.]
11:	Transfer Learning with Contextual Embeddings and Pre-trained language models		[new in this edition]

12:	Constituency Grammars		[Ch. 12 in 2nd ed.]
13:	Constituency Parsing		[expanded from Ch. 13 in 2nd ed.]
14:	Dependency Parsing		[new in this edition]

15:	Logical Representations of Sentence Meaning
16:	Computational Semantics and Semantic Parsing
17:	Information Extraction		[Ch. 22 in 2nd ed.]
18:	Word Senses and WordNet
19:	Semantic Role Labeling and Argument Structure		[expanded from parts of Ch. 19, 20 in 2nd ed]
20:	Lexicons for Sentiment, Affect, and Connotation	20: Affect [pptx] [pdf]	[new in this edition]

21:	Coreference Resolution		[mostly newly written; some sections expanded from parts of Ch 21 in 2nd ed]
22:	Discourse Coherence		[mostly new for this edition]

23:	Question Answering		[mostly newly written ; a few sections on classic algorithms expanded from parts of Ch 23 in 2nd ed]
24:	Chatbots and Dialogue Systems	24: Dialog [pptx] [pdf]	[mostly new, parts expanded from Ch 24 in 2nd ed]
25:	Phonetics		[Ch 7 in 2nd ed]
26:	Automatic Speech Recognition and Text-to-Speech		[Mostly newly written, expanded from some parts of Chs 8 and 9 in 2nd ed]

	Appendix Chapters (will be just on the web)
A:	Hidden Markov Models
B:	Spelling Correction and the Noisy Channel
C:	Statistical Constituency Parsing		[Ch. 14 in 2nd ed.]

https://web.stanford.edu/~jurafsky/slp3/

2006:

https://pages.ucsd.edu/~bakovic/compphon/Jurafsky,%20Martin.-Speech%20and%20Language%20Processing_%20An%20Introduction%20to%20Natural%20Language%20Processing%20(2007).pdf

2021:

https://web.stanford.edu/~jurafsky/slp3/ed3book_sep212021.pdf

https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf

A lexicon is the vocabulary of a language or branch of knowledge (such as nautical or medical). In linguistics, a lexicon is a language's inventory of lexemes. The word lexicon derives from Greek word λεξικόν (lexikon), neuter of λεξικός (lexikos) meaning 'of or for words'.[1] Linguistic theories generally regard human languages as consisting of two parts: a lexicon, essentially a catalogue of a language's words (its wordstock); and a grammar, a system of rules which allow for the combination of those words into meaningful sentences. The lexicon is also thought to include bound morphemes, which cannot stand alone as words (such as most affixes).[2] In some analyses, compound words and certain classes of idiomatic expressions, collocations and other phrases are also considered to be part of the lexicon. Dictionaries represent attempts at listing, in alphabetical order, the lexicon of a given language; usually, however, bound morphemes are not included.

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Overview

The Console is a REPL, which stands for Read, Evaluate, Print, and Loop. It reads the JavaScript that you type into it, evaluates your code, prints out the result of your expression, and then loops back to the first step.

Set up DevTools

This tutorial is designed so that you can open up the demo and try all the workflows yourself. When you physically follow along, you're more likely to remember the workflows later.

Press Command+Option+J (Mac) or Control+Shift+J (Windows, Linux, Chrome OS) to open the Console

Introduction

The objective of this package is simple: If you need to compute some emotion analysis on a word or a set of words this should be able to help. For now, it only supports plain text and subtitles, but the idea is to extend it to other formats (pdf, email, among other formats). In the meantime, this includes a basic example on how to use it on plain text and another example on how to use it in a collection of subtitles for series (all episodes for all seasons of a show). The name of the package is based on the limbic system <https://en.wikipedia.org/wiki/Limbic_system>__, which is a set of brain structures that support different functions, like emotions or behavior among others.

There are two strategies to compute the emotions from text supported right now:

Via lexicon-based word matching, which is quite straightforward and examples of its usage are described below.
Via a multi-label machine learning classifier trained with the specific purpose of identifying emotions and their strength in full sentences.

Limbic also has a set of tools that are easy to reuse and extend for different use cases. For example, contains tools for the analysis of subtitles in a show, but can be easily extended to analyze books, papers, websites, customer reviews, or even further applications like comparing a movie script with its book, comparing properties of movies in a sequel, among others.

https://githubmemory.com/repo/glhuilli/limbic

How To Build Attention Mechanism From Scratch

The attention mechanism was introduced to improve the performance of the encoder-decoder model for machine translation. The idea behind the attention mechanism was to permit the decoder to utilize the most relevant parts of the input sequence in a flexible manner, by a weighted combination of all of the encoded input vectors, with the most relevant vectors being attributed the highest weights.

In this tutorial, you will discover the attention mechanism and its implementation.

After completing this tutorial, you will know:

(1) How the attention mechanism uses a weighted sum of all of the encoder hidden states to flexibly focus the attention of the decoder to the most relevant parts of the input sequence.

(2) How the attention mechanism can be generalized for tasks where the information may not necessarily be related in a sequential fashion.

(3) How to implement the general attention mechanism in Python with NumPy and SciPy.

https://machinelearningmastery.com/the-attention-mechanism-from-scratch/

SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python.

SymPy Tutorial

The pandas package is the most important tool at the disposal of Data Scientists and Analysts working in Python today. The powerful machine learning and glamorous visualization tools may get all the attention, but pandas is the backbone of most data projects.

[pandas] is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals. — Wikipedia

If you're thinking about data science as a career, then it is imperative that one of the first things you do is learn pandas. In this post, we will go over the essential bits of information about pandas, including how to install it, its uses, and how it works with other common Python data analysis packages such as matplotlib and scikit-learn.