Practical Intro to NLP 23: Evolution of word vectors Part 2 - Embeddings and Sentence Transformers

Summary

This script discusses the evolution of word and sentence vector algorithms in natural language processing (NLP). It highlights the transition from TF-IDF for document comparisons to dense vector representations like Word2Vec, which addressed the limitations of sparse vectors. The script also covers the introduction of algorithms like Sense2Vec for word sense disambiguation and contextual embeddings like ELMo. It emphasizes the significance of sentence transformers, which provide context-aware embeddings and are currently state-of-the-art for NLP tasks. The practical guide suggests using TF-IDF for high-level document comparisons and sentence transformers for achieving state-of-the-art accuracies in NLP projects.

Takeaways

• 📊 Understanding the evolution of word and sentence vector algorithms is crucial for natural language processing (NLP).
• 🔍 The pros and cons of each vector algorithm should be well understood for practical application in NLP tasks.
• 📚 A baseline understanding of algorithms from TF-IDF to sentence Transformers is sufficient for many practical applications.
• 🌟 Word2Vec introduced dense embeddings, allowing for real-world operations like analogies to be performed in vector space.
• 🕵️‍♂️ Word2Vec's limitation is its inability to differentiate between different senses of a word, such as 'mouse' in computing vs. a rodent.
• 📈 Sense2Vec improves upon Word2Vec by appending parts of speech or named entity recognition tags to words, aiding in disambiguation.
• 📉 FastText addresses out-of-vocabulary words by dividing words into subtokens, but still has limitations in word sense disambiguation.
• 🌐 Contextual embeddings like ELMo and BERT capture the context of words better than previous algorithms, improving word sense disambiguation.
• 📝 Sentence embeddings, such as those from sentence Transformers, provide a more nuanced representation by considering word importance and context.
• 🏆 State-of-the-art sentence Transformers can handle varying input lengths and generate high-quality vectors for words, phrases, sentences, and documents.
• 🛠️ For lightweight tasks, TF-IDF or Word2Vec might suffice, but for state-of-the-art accuracy, sentence Transformers are recommended.

Q & A

What is the primary purpose of understanding the evolution of word and sentence vectors?

The primary purpose is to grasp the strengths and weaknesses of various word and sentence vector algorithms, enabling their appropriate application in natural language processing tasks.

How does the Word2Vec algorithm address the problem of sparse embeddings?

Word2Vec provides dense embeddings by training a neural network to predict surrounding words for a given word, which results in similar words having closer vector representations.

What is a limitation of Word2Vec when it comes to differentiating word senses?

Word2Vec struggles to differentiate between different senses of a word, such as 'mouse' referring to a computer mouse or a house mouse.

How does the Sense2Vec algorithm improve upon Word2Vec?

Sense2Vec appends parts of speech or named entity recognition tags to words during training, allowing it to differentiate between different senses of a word.

What is the main issue with using averaged word vectors for sentence embeddings?

Averaging word vectors does not capture the importance or context of individual words within the sentence, resulting in a loss of nuance.

How do contextual embeddings like ELMo address the shortcomings of Word2Vec and Sense2Vec?

Contextual embeddings like ELMo provide word vectors that are sensitive to the context in which they appear, thus improving word sense disambiguation.

What is the role of sentence transformers in generating sentence vectors?

Sentence transformers generate sentence vectors by considering the context and relationships among words, resulting in vectors that better represent the meaning of sentences.

How do algorithms like Skip-Thought Vectors and Universal Sentence Encoder improve upon traditional word vectors?

These algorithms focus on generating sentence or document vectors directly, aiming to capture the overall meaning rather than averaging individual word vectors.

What is the significance of using sentence transformers for state-of-the-art NLP projects?

Sentence transformers are currently the state of the art for generating high-quality sentence vectors, which are crucial for achieving high accuracy in NLP tasks.

How can dense embeddings from algorithms like sentence transformers be utilized for document comparison?

Dense embeddings can be used to convert documents into vectors, allowing for efficient comparison and retrieval of similar documents in vector databases.

Outlines

00:00

📚 Evolution of Word and Sentence Vectors

This paragraph discusses the evolution of word and sentence vector representations in natural language processing (NLP). It emphasizes the importance of understanding the strengths and weaknesses of various vector algorithms. The paragraph begins with an introduction to the concept of word embeddings and how they have progressed from simple vector representations to more complex, dense embeddings. It highlights the limitations of early algorithms like TF-IDF and word2vec, which struggled with word sense disambiguation and did not account for word importance within a sentence. The paragraph also introduces the concept of dense embeddings and how they allow for more nuanced operations within vector space, such as analogical reasoning. It concludes by discussing the evolution to algorithms like GloVe, which improved upon word2vec by incorporating part-of-speech tags and named entity recognition to better disambiguate word senses.

05:01

🧠 Contextual Embeddings and Sentence Vectors

The second paragraph delves into the concept of contextual embeddings, which aim to solve the issue of word sense disambiguation by considering the context in which a word is used. Algorithms like ELMo and BERT are introduced as they provide contextual embeddings that can differentiate between the same word used in different contexts. The paragraph also discusses the shift from word-level embeddings to sentence-level embeddings, which better capture the meaning of a sentence as a whole. It mentions algorithms like Skip-Thought Vectors, InferSent, and the Universal Sentence Encoder, which focus on generating sentence embeddings that are more representative of the sentence's meaning. The paragraph concludes by discussing the current state of the art in sentence embeddings, which are based on Transformer models. These models, like Sentence-BERT, are capable of generating high-quality sentence vectors that can be used for a variety of NLP tasks, including document classification and similarity comparison.

10:02

💡 Practical Applications of Sentence Vectors

This paragraph focuses on the practical applications of sentence vectors in NLP tasks. It explains how sentence vectors, unlike word vectors, are generated dynamically based on the context of the words within a sentence. The paragraph highlights the importance of considering word importance and context when generating sentence vectors, which allows for more accurate comparisons and classifications. It also discusses the efficiency of sentence vectors, which can be used to compare large volumes of text by converting documents into fixed-dimensional vectors. The paragraph provides a practical guide for choosing the right vector algorithm based on the complexity and accuracy requirements of a given task. It suggests using TF-IDF for lightweight tasks and sentence Transformers for state-of-the-art accuracy. The paragraph concludes by emphasizing the power of dense embeddings in enabling fast and efficient comparisons across large datasets.

15:03

🔍 The Future of Vector Representations in NLP

The final paragraph of the script looks towards the future of vector representations in NLP, specifically focusing on the capabilities of sentence Transformers. It discusses the versatility of these models, which can handle not just sentences but also single words, phrases, and documents, generating a single vector representation for each. The paragraph emphasizes the state-of-the-art nature of these models and their use in cutting-edge NLP projects. It also touches on the broader applications of vector representations, noting that not only text but also images can be converted into vectors for comparison and analysis. The paragraph concludes with a summary of the key points discussed in the script, reinforcing the importance of understanding the evolution and capabilities of word and sentence vectors for practical NLP applications.

Keywords

💡TF-IDF

TF-IDF, or Term Frequency-Inverse Document Frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. In the video, TF-IDF is described as a method for high-level document comparisons and classifications, but it has limitations such as the lack of word sense disambiguation and the sparsity of its vectors. It is positioned as an initial approach to understanding word importance in documents.

💡Word2Vec

Word2Vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. In the script, Word2Vec is mentioned as a significant evolution from TF-IDF, providing dense embeddings that allow for operations like analogies in vector space, such as 'king - man + woman = queen'.

💡Word Sense Disambiguation

Word Sense Disambiguation (WSD) is the process of identifying which sense (meaning) of a word is used in a particular context. The video discusses the limitations of Word2Vec in this regard, as it cannot differentiate between different senses of a word, such as 'mouse' referring to a computer device or a small rodent.

💡FastText

FastText is an extension of the Word2Vec model that allows for the understanding of rare words and phrases by treating them as compositions of their subword parts. It is mentioned in the script as a solution to the out-of-vocabulary words problem, where traditional Word2Vec might struggle.

💡Sense2Vec

Sense2Vec is an improvement over Word2Vec that attempts to address the issue of word sense disambiguation by incorporating context. In the video, it is explained as appending each word with its corresponding parts of speech or named entity recognition tags to train word vectors that can distinguish between different uses of the same word.

💡Contextual Embeddings

Contextual Embeddings are word vectors that are derived from the context in which words appear. The video discusses how algorithms like ELMo and BERT provide contextual embeddings, which are a significant step up from static word vectors like those produced by Word2Vec and FastText, as they can capture the varying meanings of words based on their surrounding text.

💡ELMo

ELMo, which stands for Embeddings from Language Models, is a deep contextualized word representation that captures the semantic meaning of words by considering the entire sentence. The video positions ELMo as a precursor to more advanced contextual embeddings, addressing the word sense disambiguation problem more effectively than Sense2Vec.

💡Sentence Embeddings

Sentence Embeddings are vector representations of sentences that capture the semantic meaning of the entire sentence. The video explains how sentence embeddings, unlike simple averages of word vectors, can weigh the importance of words within a sentence, providing a more accurate representation for tasks like similarity comparison.

💡Universal Sentence Encoder

The Universal Sentence Encoder is a model that provides a generic embedding for text, designed to work well for a wide range of tasks. It is mentioned in the script as an algorithm that deals directly with sentence-level embeddings, aiming to capture the full context of a sentence rather than relying on individual word vectors.

💡Sentence Transformers

Sentence Transformers are models that use Transformer architecture to generate sentence embeddings. They are highlighted in the video as the state-of-the-art approach for generating high-quality sentence embeddings that can capture complex relationships within sentences and are used in cutting-edge NLP projects.

Highlights

Understanding the evolution of word and sentence vectors is crucial for natural language processing (NLP).

TF-IDF is an early method for document comparison but lacks word sense disambiguation.

Word2Vec introduced dense embeddings to group similar words closer in vector space.

Word2Vec's ability to perform vector arithmetic like 'king - man + woman = queen'.

Word2Vec's limitation in differentiating between different senses of a word.

Sentence embeddings in Word2Vec are created by averaging word vectors, lacking emphasis on key terms.

Sense2Vec improves upon Word2Vec by incorporating parts of speech to differentiate word senses.

FastText addresses out-of-vocabulary words by dividing words into subtokens.

ElMo provides contextual embeddings, improving over Sense2Vec in word sense disambiguation.

Skip-thought vectors and InferSent focus on sentence-level embeddings for better context capture.

Universal Sentence Encoder and other algorithms use RNNs to generate sentence embeddings.

Sentence Transformers are state-of-the-art, using Transformer models for high-quality sentence embeddings.

Sentence Transformers can handle single words, phrases, sentences, or documents for vector generation.

Practical guide: Use TF-IDF for lightweight document comparisons and Word2Vec for dense vector needs.

For state-of-the-art accuracies in NLP, utilize Sentence Transformers.

Dense embeddings allow for efficient document comparison in vector databases.

Sentence Transformers dynamically generate vectors based on context, unlike static lookup tables.

Transcripts

so at the end of the day you need to

understand how the word and sentence

vectors have evolved or time how the

algorithms have evolved Etc and all you

need to have most of the time is to have

a strong understanding of the pros and

cons of each of the word or sentence

Vector algorithm and use them

accordingly and on any day when you need

to know more you can go and delve deeper

into a particular gorithm to make any

changes Etc but from a practical applied

NLP standpoint if you just have a good

Baseline understanding of tfidf word to

work and all the variants up to sentence

Transformers that's good enough so let's

just see how word or sentence vectors

have evolved over time so in the first

category we have

tfidf which falls into the category of

Spar and embeding and we know that V

attempted to solve this parse embedding

Problem by providing a dense embedding

because of uh its training strategy that

involved training a neural network

algorithm with corresponding surrounding

words around a given word with that we

were able to put together similar words

in a closer embedding space so the

vectors for similar words birds are

closed together and because of the power

of dense embedding as well as vectors

being closed together whatever real

world operations that we can think of we

are able to kind of emulate them in the

world Vector space as well which is King

minus man plus woman equal to queen as

well as any noun comparisons for example

plural of mouse is mice Sim Illy

elephant is elephants Etc so you can do

comparisons like mice minus Mouse plus

elephant is equal to elephants Etc and

we have seen the problem with what to W

which is it cannot differentiate between

different senses of a word for example

between computer mouse and the house

mouse and also we have seen that when it

comes to sentence

embedding we are just averaging our the

individual word vectors so there is no

weightage or important given to certain

keywords Etc and the next Evolution from

word to W is actually sense to W so

during training what we can do is we can

append each word with its

corresponding POS parts of speech or any

or named entity recognition tag what I

mean by that is let's say you have a

word like Bank

Bank could be a

verb Bank could be a noun Financial Bank

whereas verb could be like depend on

somebody banking on somebody Etc so in

such cases all you can do is in the

context where bank is verb you can

replace Bank by bankor V and in the

context where bank is noun you can

replace Bank uncore n and you can train

a word Vector algorithm parts of speech

appended entities so that when during

query time when you give bankor n

instead of getting Depend and other

things as the near word vectors you'll

mostly get financial bank related word

vectors like loan Etc that's what you

can do so sense to is actually an

improvement over V to where you can

actually append the sense of a given

word even

and also you have seen that to counter

out of vocabulary words we have fast

text which can divide a given word into

its sub tokens again sense to is limited

because when you take the case of mouse

mouse is a noun both in both computer

mouse as well as the house mouse so you

cannot really differentiate with the

parts of speech

tag the other thing that you can do is

give a named and idti recognition n tag

as well appended to it but like I

mentioned still it has some shortcomings

so over TF IDF we have word to work and

over word to we have sense to work but

still we do have some issues which is

which are one we are unable to fully

resolve the context of a given word so

we are unable to do word sense

disambiguation so we still need

contextual

embeddings secondly we don't really have

word importance captured because we are

still doing look up for a given word and

getting its word embedding and when

you're getting sentence Vector you are

just plainly averaging the word vectors

whereas we want to also calculate the

importance of a given word so we want to

change the weights or we want a single

word word to have more context and its

embedding should come from Context

across the words that are surrounding it

and Elmo and B fall into this category

of contextual embeddings so essentially

if you give a sentence for example I'm

going to the bank to deposit some money

in that case automatically the word bank

will have a different word

embedding when compared to to another

sense where I'm going to the river bank

in both the sentences bank is a noun but

since the context is different you'll

have contextual embeddings because of

algorithms like Elmo and B etc for

Simplicity you can think of Elmo as the

first contextual embedding that came out

which tried to solve this word sense

disambiguation problem that you had with

mouse Bank Etc and then came algorithms

that specifically focused on sentence to

W approaches we just realized with wck

that although we are able to get the

perfect embedding for a given word just

averaging the word vectors of words in a

given sentence and getting a sentence

Vector doesn't really suffice because

plain averaging doesn't capture the

complete picture of a sentence whereas

we should be able to do some smart

averaging across the word vectors of

individual words in a sentence so in

order to solve that several algorithms

like skip thought vectors inferent and

Universal sentence encoder came into

existence which is instead of dealing on

a word level they directly deal on a

sentence level to give a unique Vector

for the whole sentence such that similar

sentences are closer together and these

algorithms skip thought vectors inferent

Universal sentence encoder they most of

them used RNN recurrent neural networks

like lstms and grus in order to generate

these sentence embeddings of course

Universal sentence encoder Had A

variation which used Transformers which

is the advanced

algorithms that captures relationships

among different words

in order to get an embedding for a given

word and moving further on current state

of the art is actually sentence

Transformers so these are Transformer

based algorithms that are trained on top

of bird Etc to encode sentences

specifically to keep similar sentences

closer together in the vector space and

dissimilar sentences farther together in

the vector space so they are fine tuned

on top of

bir like algorithms to produce high

quality sentence embeddings and one

thing to remember is that not only words

or sentences or paragraphs are converted

into Vector even images could be

converted into vectors so there are

models like clip and uh several Vision

algorithms that can convert a given

image into a single Vector of fixed

Dimensions so that you can compare

compare images among each other Etc so

if you come to sentence Vector sentence

Vector improves on top of the word

Vector so you can see that the biggest

shortcoming of word to V was there was

no Word Sense disambiguation secondly

the average Vector was pretty simple

just averaging across all the words so

sentence Vector essentially solves that

problem where although it it is termed

as sentence Vector you can just give an

individual word or just a word phrase

even and get Vector out of it for

example you can just give a single word

like Batman and get a vector or you can

give multi-word like Donald Trump or Joe

Biden Etc and also get a vector or you

can give a sentence and a whole

paragraph and get a vector so at the end

of the day again just like ver

sentence vectors that is sentence

Transformers can convert a single word

or just a phrase into a vector directly

of 768 dimensions and again a sentence

could be converted into a single Vector

of 768 Dimensions the only difference is

that in word Vector algorithms we just

averaged out all the individual word

vectors in a given sentence whereas when

it comes to sentence vectors

what happens is that for example a given

word like

directed directed does not independently

have a vector of its own rather the

directed

Vector comes from a combination of all

the surrounding words to the left and

right of this so you can so you can kind

of imagine that whatever we wanted to do

which is smart weighted averaging of

word vectors in V algorithm we kind of

do that in sentence Transformer

inherently where each individual Vector

that we get is actually a smart weighted

combination of other vectors and at the

end we also combine average out all the

vectors that we have in order to get the

single smart Vector but remember that in

V we have kind of a lookup table

scenario where individual word and its

Vector is obtained whereas in this case

this individual Vector is not

pre-calculated and kept in a lookup

table but rather generated dynamically

depending on the sentence so that Vector

is very efficient and it captures all

these complexities like Word Sense

disambiguation like Mouse in both cases

as well as the relationship of that word

with respect to the corresponding words

that are to the left and right so you

can assume that unlike word vectors this

is a smart Vector that is of 768

Dimensions similarly for paragraph also

it's the same we essentially have a max

length usually one24 tokens or or tokens

converted to some word count like 52 Etc

so sentence vectors Etc can take let's

say a paragraph of Maximum 500 Words Etc

and it can generate a smart Vector even

so at the end of the day a word is

represented by a single Vector a noun

phrase or verb phrase that is multi-word

is also represented by a single Vector a

sentence is represented by a single

Vector a whole paragraph or document is

also represented by a single Vector so

now you can do easy comparisons among

each other so just to recap everything

that we have learned we have TF Ida for

high high level document comparisons and

classifications as well but it has quite

a few shortcomings one is there is no

word similarity that is embedded second

is the vectors are very SP very sparse

most of them are zeros so we moved from

that to dense vectors that is instead of

having 50,000 length we have now 360

Dimensions or 768 Dimensions length

vector so V was one such algorithm in

order to tackle

subwords we have fast text and in order

to have Word Sense disambiguation we

introduced sense Toc and beyond that we

have Elmo which actually gives

contextual embeddings and performs

better than sens to so I just kept all

of these algorithms from ver to Elmo in

this dense word vectors because each of

them improves or the other by solving

some of the shortcomings of the before

one now we realize that just getting

vectors for word isn't sufficient we

need to do better that is smart

averaging to get a sentence Vector

because a sentence Vector is not just

plain averaging of individual word

Vector we need to have some kind of

weight or importance Factor also

captured in the sentence Vector when we

are getting the combined things

so these algorithms like skip thought

vectors inferent Universal sentence

encod or USC improved on word vectors

and tried to get us sentence vectors or

document vectors straight away that are

of higher quality that we can use for

comparisons but still they had some

shortcomings in the sense that the

vectors are not of high

quality and then came sentence

Transformer

which is on based on Transformer based

algorithms for sentence vectors although

it is called as sentence Vector it can

take a single word or a multi-word

phrase or a sentence or a document and

generate Vector single Vector of let's

say 768

dimensions and this is currently the

state of the art the sentence

Transformers and its variants and they

are the ones that are used in Cutting

Edge State ofthe art projects NLP

projects so the Practical guide is that

if you're doing lightweight

classification or high level comparisons

you can use tfidf vectors if you have

lot of Corpus if you don't have the

Corpus and you want to do again

lightweight classification and

comparisons with dense vectors you can

go for water because you don't need to

have any Corpus or pre-train anything so

averaged word vector and doing

classifications or comparisons is a good

Baseline but if you really want to hit

it out and get state of the art

accuracies you can use sentence

Transformers and the power with dense

embeddings is that even if you have 1

million or 10 million documents you can

just convert each of the document into a

single Vector using sentence

Transformers and you can place all those

1 million or 10 million documents in a

Vector database that is specifically

designed for comparing vectors among

each other now let's say you have a new

movie plot or new document you can

convert that into a vector and do very

fast

comparisons across all the 1 million or

10 million documents that you have

comparing the vectors to fetch the most

similar vectors Etc so essentially

remember this thing if you don't want to

do heavy computation and want to do

something very quickly you can use

pre-trained what to algorithm and just

get your embeddings and average

embeddings and do

comparisons that's rule number one to

remember rule number two if you want to

get state of the accuracies go for

sentence Transformers thanks for

watching