This document provides an overview of natural language processing (NLP) including:
1. An introduction to NLP and its intersection with computational linguistics, computer science, and statistics.
2. A discussion of common NLP problems like tokenization, tagging, parsing, and their rule-based and statistical approaches.
3. An explanation of machine learning techniques for NLP like language models, naive Bayes classifiers, and dependency parsing.
4. Steps for developing an NLP system including translating requirements, experimentation, and going to production.
What one needs to know to work in Natural Language Processing field and the aspects of developing an NLP project using the example of a system to identify text language
What one needs to know to work in Natural Language Processing field and the aspects of developing an NLP project using the example of a system to identify text language
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
A general method applicable to the search for anglicisms in russian social ne...Ilia Karpov
In the process of globalization, the number of English words in other languages has rapidly increased. In automatic speech recognition systems, spell-checking, tagging, and other software in the field of natural language processing,
loan words are not easily recognized and should be evaluated
separately. In this paper we present a corpora-based approach to the automatic detection of anglicisms in Russian social network
texts. Proposed method is based on the idea of simultaneous
scripting, phonetics, and semantics similarity of the original Latin word and its Cyrillic analogue. We used a set of transliteration, phonetic transcribing, and morphological analysis methods to find possible hypotheses and distributional semantic models to filter them. Resulting list of borrowings, gathered from approximately 20 million LiveJournal texts, shows good intersection with manually collected dictionary. Proposed method is fully automated and can be applied to any domain–specific area.
Full paper available at:
https://www.academia.edu/29834070/A_General_Method_Applicable_to_the_Search_for_Anglicisms_in_Russian_Social_Network_Texts
Information Extraction, Named Entity Recognition, NER, text analytics, text mining, e-discovery, unstructured data, structured data, calendaring, standard evaluation per entity, standard evaluation per token, sequence classifier, sequence labeling, word shapes, semantic analysis in language technology
In this presentation we discuss several concepts that include Word Representation using SVD as well as neural networks based techniques. In addition we also cover core concepts such as cosine similarity, atomic and distributed representations.
A general method applicable to the search for anglicisms in russian social ne...Ilia Karpov
In the process of globalization, the number of English words in other languages has rapidly increased. In automatic speech recognition systems, spell-checking, tagging, and other software in the field of natural language processing,
loan words are not easily recognized and should be evaluated
separately. In this paper we present a corpora-based approach to the automatic detection of anglicisms in Russian social network
texts. Proposed method is based on the idea of simultaneous
scripting, phonetics, and semantics similarity of the original Latin word and its Cyrillic analogue. We used a set of transliteration, phonetic transcribing, and morphological analysis methods to find possible hypotheses and distributional semantic models to filter them. Resulting list of borrowings, gathered from approximately 20 million LiveJournal texts, shows good intersection with manually collected dictionary. Proposed method is fully automated and can be applied to any domain–specific area.
Full paper available at:
https://www.academia.edu/29834070/A_General_Method_Applicable_to_the_Search_for_Anglicisms_in_Russian_Social_Network_Texts
Information Extraction, Named Entity Recognition, NER, text analytics, text mining, e-discovery, unstructured data, structured data, calendaring, standard evaluation per entity, standard evaluation per token, sequence classifier, sequence labeling, word shapes, semantic analysis in language technology
Deep dive into the mathematics and algorithms of neural nets. Covers the sigmoid activation function, cross-entropy loss function, gradient descent and the derivatives used in back propagation.
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This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
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Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
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Sectoral targets and attacks as well as the cost of ransom
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Cyber risk predictions
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The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
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In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
2. A Bit about Me
* Lisp programmer
* 5+ years of NLP work at Grammarly
* Occasional lecturer
https://vseloved.github.io
3. A Bit about Grammarly
The best English language writing app
Spellcheck - Grammar check - Style
improvement - Synonyms and word choice
Plagiarism check
4. Plan
* Overview of NLP
* Where to get Data
* Common NLP problems
and approaches
* How to develop an NLP
system
6. What Is NLP?
Transforming free-form text
into structured data and back
Intersection of:
* Computational Linguistics
* CompSci & AI
* Stats & Information Theory
9. Time flies like an arrow.
Fruit flies like a banana.
I read a story about evolution in ten minutes.
I read a story about evolution in the last million years.
10. NLP & Data
Types of text data:
* structured
* semi-structured
* unstructured
“Data is ten times more
powerful than algorithms.”
-- Peter Norvig
The Unreasonable Effectiveness of Data.
http://youtu.be/yvDCzhbjYWs
11. Kinds of Data
* Dictionaries
* Databases/Ontologies
* Corpora
* User Data
12. Where to Get Data?
* Linguistic Data Consortium
http://www.ldc.upenn.edu/
* Common Crawl
* Wikimedia
* Wordnet
* APIs: Twitter, Wordnik, ...
* University sites &
the academic community:
Stanford, Oxford, CMU, ...
15. Tokenization
Example:
This is a test that isn't so simple: 1.23.
"This" "is" "a" "test" "that" "is" "n't"
"so" "simple" ":" "1.23" "."
Issues:
* Finland’s capital -
Finland Finlands Finland’s
* what’re, I’m, isn’t -
what ’re, I ’m, is n’t
* Hewlett-Packard or Hewlett Packard
* San Francisco - one token or two?
* m.p.h., PhD.
16. Regular Expressions
Simplest regex: [^s]+
More advanced regex:
w+|[!"#$%&'*+,./:;<=>?@^`~…() {}[|]⟨⟩ ‒–—
«»“”‘’-]―
Even more advanced regex:
[+-]?[0-9](?:[0-9,.]*[0-9])?
|[w@](?:[w'’`@-][w']|[w'][w@'’`-])*[w']?
|["#$%&*+,/:;<=>@^`~…() {}[|] «»“”‘’']⟨⟩ ‒–—―
|[.!?]+
|-+
17. Post-processing
* concatenate abbreviations and decimals
* split contractions with regexes
2-character:
i['‘’`]m|(?:s?he|it)['‘’`]s|(?:i|you|s?he|we|they)
['‘’`]d$
3-character:
(?:i|you|s?he|we|they)['‘’`](?:ll|[vr]e)|n['‘’`]t$
18. Rule-based Approach
* easy to understand and
reason about
* can be arbitrarily precise
* iterative, can be used to
gather more data
Limitations:
* recall problems
* poor adaptability
22. Language Models
Question: what is the probability of a
sequence of words/sentence?
Answer: Apply the chain rule
P(S) = P(w0) * P(w1|w0) * P(w2|w0 w1)
* P(w3|w0 w1 w2) * …
where S = w0 w1 w2 …
23. Ngrams
Apply Markov assumption: each word depends
only on N previous words (in practice
N=1..4 which results in bigrams-fivegrams,
because we include the current word also).
If n=2:
P(S) = P(w0) * P(w1|w0) * P(w2|w0 w1)
* P(w3|w1 w2) * …
According to the chain rule:
P(w2|w0 w1) = P(w0 w1 w2) / P(w0 w1)
24. Spelling Correction
Problem: given an out-of-dictionary
word return a list of most probable
in-dictionary corrections.
http://norvig.com/spell-correct.html
25. Edit Distance
Minimum-edit (Levenstein) distance the–
minimum number of
insertions/deletions/substitutions needed
to transform string A into B.
Other distance metrics:
* the Damerau-Levenstein distance adds
another operation: transposition
* the longest common subsequence (LCS)
metric allows only insertion and deletion,
not substitution
* the Hamming distance allows only
substitution, hence, it only applies to
strings of the same length
26. Dynamic Programming
Initialization:
D(i,0) = i
D(0,j) = j
Recurrence relation:
For each i = 1..M
For each j = 1..N
D(i,j) = D(i-1,j-1), if X(i) = Y(j)
otherwise:
min D(i-1,j) + w_del(Y(j))
D(i,j-1) + w_ins(X(i))
D(i-1,j-1) + w_subst(X(i),Y(j))
27. Noisy Channel Model
Given an alphabet A, let A* be the set of all finite
strings over A. Let the dictionary D of valid words be
some subset of A*.
The noisy channel is the matrix G = P(s|w) where w in D is
the intended word and s in A* is the scrambled word that
was actually received.
P(s|w) = sum(P(x(i)|y(i)))
for x(i) in s* (s aligned with w)
for y(i) in w* (w aligned with s)
29. Spam Filtering
A 2-class classification problem with a
bias towards minimizing FPs.
Default approach: rule-based (SpamAssassin)
Problems:
* scales poorly
* hard to reach arbitrary precision
* hard to rank the importance of
complex features?
30. Bag-of-words Models
* each word is a feature
* each word is independent of others
* position of the word in a sentence is irrelevant
Pros:
* simple
* fast
* scalable
Limitations:
* independence assumption doesn't hold
Initial results: recall: 92%, precision: 98.84%
Improved results: recall: 99.5%, precision: 99.97%
http://www.paulgraham.com/spam.html
31. Naive Bayes
Classifier
P(Y|X) = P(Y) * P(X|Y) / P(X)
select Y = argmax P(Y|x)
Naive step:
P(Y|x) = P(Y) * prod(P(x|Y))
for all x in X
(P(x) is marginalized out because it's the
same for all Y)
35. ML-based Parsing
The parser starts with an empty stack, and a buffer index at 0, with no
dependencies recorded. It chooses one of the valid actions, and applies it to
the state. It continues choosing actions and applying them until the stack is
empty and the buffer index is at the end of the input.
SHIFT = 0; RIGHT = 1; LEFT = 2
MOVES = [SHIFT, RIGHT, LEFT]
def parse(words, tags):
n = len(words)
deps = init_deps(n)
idx = 1
stack = [0]
while stack or idx < n:
features = extract_features(words, tags, idx, n, stack, deps)
scores = score(features)
valid_moves = get_valid_moves(i, n, len(stack))
next_move = max(valid_moves, key=lambda move: scores[move])
idx = transition(next_move, idx, stack, parse)
return tags, parse
36. Averaged Perceptron
def train(model, number_iter, examples):
for i in range(number_iter):
for features, true_tag in examples:
guess = model.predict(features)
if guess != true_tag:
for f in features:
model.weights[f][true_tag] += 1
model.weights[f][guess] -= 1
random.shuffle(examples)
37. Features
* Word and tag unigrams, bigrams, trigrams
* The first three words of the buffer
* The top three words of the stack
* The two leftmost children of the top of
the stack
* The two rightmost children of the top of
the stack
* The two leftmost children of the first
word in the buffer
* Distance between top of buffer and stack
38. Discriminative ML
Models
Linear:
* (Averaged) Perceptron
* Maximum Entropy / LogLinear / Logistic
Regression; Conditional Random Field
* SVM
Non-linear:
* Decision Trees, Random Forests
* Other ensemble classifiers
* Neural networks
43. Word Similarity
Next question: now, how do we measure those
relations?
* different Wordnet similarity measures
* PMI(x,y) = log(p(x,y) / p(x) * p(y))
44. Distributional
Semantics
Distributional hypothesis:
"You shall know a word by
the company it keeps"
--John Rupert Firth
Word representations:
* Explicit representation
Number of nonzero dimensions:
max:474234, min:3, mean:1595, median:415
* Dense representation (word2vec, GloVe)
* Hierarchical representation
(Brown clustering)
45. Steps to Develop
an NLP System
* Translate real-world requirements
into a measurable goal
* Find a suitable level and
representation
* Find initial data for experiments
* Find and utilize existing tools and
Frameworks where possible
* Don't trust research results
* Setup and perform a proper
experiment (series of experiments)
46. Going into Prod
* NLP tasks are usually CPU-intensive
but stateless
* General-purpose NLP frameworks are
(mostly) not production-ready
* Value pre- and post- processing
* Gather user feedback
47. Final Words
We have discussed:
* linguistic basis of NLP
- although some people manage to do NLP
without it:
http://arxiv.org/pdf/1103.0398.pdf
* rule-based & statistical/ML approaches
* different concrete tasks
We haven't covered:
* all the different tasks, such as MT,
question answering, etc.
(but they use the same technics)
* deep learning for NLP
* natural language understanding
(which remains an unsolved problem)