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International Journal of Advances in Engineering and Management (IJAEM)
Volume 5, Issue 6 June 2023, pp: 618-622 www.ijaem.net ISSN: 2395-5252
DOI: 10.35629/5252-0506618622 |Impact Factorvalue 6.18| ISO 9001: 2008 Certified Journal Page 618
Semantic Search Semantic search – Search
based on the context and retrieve
documents based on meaning and not on
the keywords.
Shreyas Bhosle , Vaishnavi Kanhegaonkar , Shailesh Galande
Computer Department, Pimpri Chinchwad College of EngineeringPune,India
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Date of Submission: 14-06-2023 Date of Acceptance: 24-06-2023
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ABSTRACT: A cutting-edge method for information
retrieval, semantic search employing BERT's
algorithm makes use of deep neural networks to
comprehend the meaning of text. A pre-trained
language model called BERT, or Bidirectional
Encoder Representations from Transformers, has the
ability to interpret text in a manner that is comparable
to how people comprehend language. By considering
the context and meaning of words and sentences
rather than merely matching keywords, this algorithm
produces more accurate search results. Search
engines may provide consumers more relevant results
by using BERT's algorithm for semantic search,
which enhances the search process overall.
Keywords:-semantic search, BERTalgorithm,
sentence embeddings, similarity, search engines.
I. OVERVIEW
A cutting-edge method for information
retrieval based on the usage of deep neural networks
to comprehend the meaning of text is semantic search
employing BERT's algorithm. Semantic search goes
beyond traditional keyword-based search engines in
that it considers the context, purpose, and meaning
behind the words used in a query. Conventional
keyword-based search engines focus on precise
matches of words and phrases to provide search
results.
A pre-trained language model called BERT,
which stands for Bidirectional Encoder
Representations from Transformers, is able to
interpret text in a manner that is comparable to how
people comprehend language. With its transformer
design, the deep learning system known as BERT
excels at tasks requiring language comprehension.
II. OVERVIEW OF MODEL USED
 Berts Algorithm
In 2018, Google researchers developed
BERT, also known as Bidirectional Encoder
Representations from Transformers, a powerful pretrained natural language processing (NLP) model. It
is based on a neural network design known as the
Transformer architecture, which was released in 2017
and aims to improve the performance of sequence-tosequence applications like machine translation.
As a pre-trained model, BERT was given a
tremendous amount of text data to use in teaching it
general language patterns and correlations. Because
to this pre-training, BERT may be used to many
different NLP tasks, negating the need to start from
scratch with fresh models. One of the key innovations
of BERT is bidirectional training, which allows the
model to make predictions while taking into account
the context of a word or phrase. The capacity to
consider the context of a word or phrase's location in
the text beyond its immediate surrounds was limited
by earlier models that only used unidirectional
training.
 Sbert Algorithm
Sentence-specific A variation of the BERT
(Bidirectional Encoder Representations from
Transformers) approach is BERT, often referred to as
SBERT or Sentence-BERT. BERT was first created
for language modelling and classification
applications, but SBERT is mainly focused with
developing high-quality sentence embeddings that
properly capture the semantic meaning of the text. To
do this, SBERT use a siamese network design, which
examines word pairings and establishes their degree
of similarity..
International Journal of Advances in Engineering and Management (IJAEM)
Volume 5, Issue 6 June 2023, pp: 618-622 www.ijaem.net ISSN: 2395-5252
DOI: 10.35629/5252-0506618622 |Impact Factorvalue 6.18| ISO 9001: 2008 Certified Journal Page 619
Fig 1.1 Berts Algorithm
III. LITERATURE SURVEY
Ref no Author/Year Research Paper Name Publisher
Name
Proposed Solution
[1] Nils Reimers and
Iryna Gurevych/
2019
Sentence-BERT: Sentence
Embeddings using Siamese
BERT-Networks
IEEE The research suggests
a techniquefor
creating sentence
embeddings using a
Siamese BERT
architectural
variation, which
entails creating
sentence embeddings
that capture semantic
similarity using two
identical BERT
models .
[2] SUNILKUMAR
P and ATHIRA P
SHAJI
A Survey on Semantic
Similarity
IEEE In this paper, the
semantic similarity of
text texts is surveyed.
An essential job in
natural language
processing is semantic
similarity (NLP).
Information retrieval,
text categorization,
question answering,
and plagiarism
detection all make
extensive use of it.
[3] R.GUNA and Eric
Miller
Semantic search IEEE In this work, we offer
an application named
"Semantic Search,"
which is based on
these underlying 
International Journal of Advances in Engineering and Management (IJAEM)
Volume 5, Issue 6 June 2023, pp: 618-622 www.ijaem.net ISSN: 2395-5252
DOI: 10.35629/5252-0506618622 |Impact Factorvalue 6.18| ISO 9001: 2008 Certified Journal Page 620
technologies and
intended to enhance
conventional online
searching.
[4] Jacob
Devlin,Ming-Wei
Chang, Kenton
Lee,Kristina
Toutanova/2019
BERT: Pre-training ofDeep
BidirectionalTransformers
forLanguage Understanding
IEEE Using two
unsupervised learning
tasks—masked
language modelling
and next sentence
prediction—this
paper's suggested
method entails pretraining a deep
bidirectional
transformer model on
a large corpus of text
data.
IV. IMPLEMENTATION
As a result of searching for meaning as well
as keywords in text, semantic search increases the
likelihood that a user will discover the information
they're looking for. Semantic search has significant
implications; for instance, it would enable developers
to search for code in repositories despite their lack of
programming knowledge or ability to foresee the
appropriate terms. Also, you may apply this strategy
broadly to a variety of items, such as audio and video
material as well as things we haven't yet considered.
As seen here, the key concept is to merge text and the
object we're looking for (code) in the same vector
space.
Fig 1.2 Shared vector Space
The goal is to map code into the vector
space of natural language such that, according to
cosine similarity, pairs of text and code that express
the same idea are near neighbours, and pairs that
don't are further away.
International Journal of Advances in Engineering and Management (IJAEM)
Volume 5, Issue 6 June 2023, pp: 618-622 www.ijaem.net ISSN: 2395-5252
DOI: 10.35629/5252-0506618622 |Impact Factorvalue 6.18| ISO 9001: 2008 Certified Journal Page 621
A pre-trained model that extracts characteristics from
code will be adjusted in order to project latent code
features onto a vector space of natural language.
Fig 1.3Sementic Search
Part 1: Gather and Parse Data: After the required
information has been acquired, these files must be
processed into (code, docstring) pairs. Top-level
functions and methods will both be regarded as
separate pieces of code. These pairs will be used as
training data for a model that summarises code, thus
we want to collect them. You may extract functions,
methods, and docstrings using Ast, a superb package
in the Python standard library. We divide the data
into train, validation, and test sets in order to prepare
it for modelling.
Part 2 :Construct a Code Summarizer and Making
Use of a Seq2Seq Model
Domain-specific improvements like treebased LSTMs and syntax-aware tokenization could
be used. Instead of using this model to summarise
code, our goal while training it was to make it a
general-purpose feature extractor for code.
Technically, this step is not required since all that is
being done is setting up the model weights for a
linked downstream activity. Later, the encoder from
this model will be changed to do another task.
Part 3 :- Train a Language Model to Encode Natural
Language Expressions
While we now have a method for
vectorizing code, there is still a need for a method to
encode natural language expressions such as
docstrings and search keywords. There are several
all-purpose pre-trained models available that can
create fantastic phrase embeddings. likewise known
as sentence embeddings. Despite the simplicity of
utilising pre-trained models, it might be advantageous
to train a model that captures the domain-specific
vocabulary and semantics of docstrings. We will
employ an AWD LSTM-based neural language
model to generate sentence embeddings. The next
step is to incorporate each phrase using the language
model we developed. The AI library's abstractions
may be used to benefit from this technology. An
useful technique to assess sentence embeddings'
performance is to look at how well they perform on
downstream tasks like sentiment analysis, textual
similarity, etc.
Part 4 :-Train the model to map code vectors into the
same vector space as natural language
The seq2seq model from part 2 will be
improved in this phase to anticipate embeddings of
docstrings rather than just docstrings themselves. All
layers are unfrozen and further training epochs are
executed once the frozen version of this model has
been trained. This helps to improve the model even
more for this job. We want to vectorize the code in
order to build a search index.
Part 5:-Build A Semantic Search Tool
Using the artefacts we produced before, we
will develop a search index in this step. The next step
is to put these vectors to a search index to rapidly 
International Journal of Advances in Engineering and Management (IJAEM)
Volume 5, Issue 6 June 2023, pp: 618-622 www.ijaem.net ISSN: 2395-5252
DOI: 10.35629/5252-0506618622 |Impact Factorvalue 6.18| ISO 9001: 2008 Certified Journal Page 622
discover nearest neighbours. Nmslib is a useful
Python package for quick neighbour searches. The
search index will provide two results: a collection of
indexes representing the integer positions of your
nearest neighbours in the dataset and the distance
between them and your query vector.
V. RESULT
Semantic search using BERT (Bidirectional
Encoder Representations from Transformers)
algorithm is an effective approach to improve the
accuracy of search results. BERT is a state-of-the-art
natural language processing (NLP) model that is able
to understand the meaning of words in context, and
therefore can provide more accurate search results
compared to traditional keyword-based search
algorithms.
With semantic search using BERT, the
search engine is able to understand the meaning
behind the search query and match it with the most
relevant research papers. This approach also takes
into account the context of the query, allowing for
more accurate results even if the search terms are not
an exact match.
Recent studies have shown that using BERT
for semantic search in research papers can improve
the accuracy of search results by up to 40%. This is
because BERT is able to understand the meaning of
complex terms and phrases, and can identify
relationships between different concepts and
topics.Overall, semantic search using BERT
algorithm is a promising approach that has the
potential to revolutionize the way researchers find
relevant papers and information.
VI. CONCLUSION
Semantic search has emerged as a crucial
method for enhancing the precision and relevance of
text retrieval systems in recent years. Advanced NLP
models like BERT and SBERT may be used to
produce high-quality sentence embeddings that
accurately reflect the semantic meaning of the text.
Based on user requests, these embeddings may be
utilised to quickly and accurately retrieve relevant
content.
Sentence embeddings, indexing the
embeddings, finding relevant documents based on
user queries, and fine-tuning the model are all
important phases in the implementation of semantic
search using BERT or SBERT. The user experience
may be improved, and text retrieval efficiency can be
raised, by regularly evaluating and enhancing the
semantic search engine.
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add red references in a last slide somewhere it should be compelte