A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMS

International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.2, March 2023
DOI: 10.5121/ijaia.2023.14204 41
A COMPARISON OF DOCUMENT SIMILARITY
ALGORITHMS
Nicholas Gahman and Vinayak Elangovan
Computer Science Program, Division of Science and Engineering,
Penn State University at Abington, Pennsylvania, USA
ABSTRACT
Document similarity is an important part of Natural Language Processing and is most commonly used for
plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity
algorithm could have a major positive impact on the field of Natural Language Processing. This report sets
out to examine the numerous document similarity algorithms, and determine which ones are the most
useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of
document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in our work using a series of
benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
KEYWORDS
Natural Language Processing, Document Similarity
1. INTRODUCTION
Document similarity analysis is a Natural Language Processing (NLP) task where two or more
documents are analyzed to recognize the similarities between these documents. Document
similarity is heavily used in text summarization, recommender systems, plagiarism-detection as
well as in search engines. Identifying the level of similarity or dissimilarity between two or more
documents based on their content is the main objective of document similarity analysis.
Commonly used techniques like cosine similarity, Euclidean distance, etc., compares the
document’s text features to provide a similarity score between 0 and 1, where 1 indicates
complete similarity and 0 indicates no similarity. Although there are numerous algorithms used
for document similarity, there are no algorithms universally recognized as the most effective or
efficient in a given area.
This paper seeks to rectify this issue by categorizing the document similarity algorithms into 3
different types of document similarity algorithms: statistical algorithms, neural networks, and
corpus/knowledge-based algorithms. Each category’s algorithms will be closely inspected, and
several algorithms from each category will be judged to be the best of said category. Once the
most effective algorithms of each category are found, they will be run through four different
datasets and three different metrics. Finally, once this data is obtained, it will be analyzed to
determine which of the algorithms are most effective.
This paper is organized as follows: In section 2: Relevant Works, a variety of similar research
work are discussed. In section 3: Proposed Methodology: a general description of three
categories of document similarity algorithms is given, and the methodology and the comparison
process are more thoroughly discussed. Finally, in section 4: Results, the specifics, and

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limitations of implementation of each algorithm are discussed, the results of the comparison
process are given followed by conclusions in section 5.
2. RELEVANT WORKS
Jesus M. Sanchez-Gomez, along with other researchers, compared term-weighting schemes and
similarity measures when used for extractive multi-document text summarization [1]. The result
of this research is that overall cosine similarity appears to be the most effective statistical
similarity measure in this area. Kazuhiro Seki created a text similarity measure that was able to
work across languages [2]. The proposal accomplished this by using two Neural Machine
Translation models to build word embeddings that it can use to compute document similarity.
The NMTs in question translated multiple possible translations to account for mistranslations.
The result of the translations are matrices that are normalized and transformed, creating
multilingual word embeddings that can be compared using cosine similarity. The proposal has
significant flexibility, as the study explicitly states that it is possible to add other similarity scores
onto this system. This proposal was compared to other multilingual similarity algorithms such as
Doc2Vec, Sec2Vec, and the S2Net Siamese Neural Network, with the result being that it
outperformed all other algorithms. When the proposal’s sentence retrieval was compared to
Google Translate, they had similar results, despite the proposal having a much lower BLEU score
due to its relatively small training data and models.
Emrah Inam proposed a document similarity algorithm that combines the usage of word
embeddings with knowledge-based measures through a semantic lexicon named ConceptNet [3].
Once the sentences are run through ConceptNet, the proposal then produces a vector
representation of the transformed sentences using a breadth first traversal algorithm. Finally, a
soft cosine similarity measure is used to compare the vectorized sentences. The similarity from
the ConceptNet metric and the similarity from the dependency parser model is then combined to
produce the final similarity score. The proposal, named SimiT, is given the Microsoft Research
Paraphrase Corpus (MRPC) as an evaluation task to determine if it can detect and understand
paraphrases. Its result is measured using Pearson correlation and is then compared to several
other similarity algorithms’ results. Among these algorithms are basic cosine similarity,
Word2Vec cosine and soft cosine, and several state-of-the-art methods such as BERT and its
variations. Of the compared algorithms, SimiT performed very well, exceeding the performance
of all but the state-of-the-art methods. Even among the state-of-the-art methods, SimiT was still
useful due to its incredibly low run time.
Table 1. Chosen dataset’s tasks and purpose.
MRPC AFS SICK-R SICK-E
Benchmark
Purpose
Testing if algorithm
can detect
paraphrases
Testing if
algorithm can
detect similar
arguments
Testing if
algorithm can see
lexical similarity
of sentences
Testing if algorithm
can see semantic
similarities/differen
ces between
sentences
Aminul Islam and Diana Inkpen collaborated on a method to give knowledge-based measures the
ability to determine sentence similarity [4]. Knowledge-based measures are designed primarily
to compare concepts, and while turning a word into a concept is easy it is much harder to do so
for a sentence. As part of the process, the authors introduce a string similarity word method
using three modifications of the longest common subsequence: The Normalized Longest

43
Common Subsequence (NLCS), the Normalized Maximal Consecutive Longest Common
Subsequence starting from the first character (NMCLCS1), and the Normalized Maximal
Consecutive Longest Common Subsequence starting from any character (NMCLCSn). The scores
from each of the three modified LCS algorithms are combined, then divided by three, such that
there is an average between 0 and 1.
To develop a sentence similarity algorithm that uses knowledge-based measures, the authors first
process the sentences by removing all special characters, punctuation, capital letters, and stop
words. At this point, the lengths of the two sentences are stored for later use. From here, they
then find all of the words in both sentences that match with each other using the knowledge-
based measures or string similarity measures, count the number of words where this is the case,
and remove all matching words from the sentence. Then, all the remaining words are compared
using the knowledge-based measure, and the results are put in a matrix whose length is equal to
the length of the first sentence and width is equal to the length of the second sentence. Another
matrix is created using the same method, with the string similarity method replacing the
knowledge-based measure. The knowledge-based measure matrix is added with the string
similarity matrix to produce a joint matrix. Next, the highest value in the joint matrix is found
and added to a list, after which the column and row the value was in is deleted. This continues
until the highest value is zero or until there are no rows or columns left. The final algorithm to
determine the similarity of the two sentences is the number of matching words plus all the values
in the list, times the length of the two sentences without stop words combined. The result is then
divided by two times the length of the first sentence without stop words, times the length of the
second sentence without stop words. Table 2 summarizes the advantages and disadvantages of
different algorithm categories.
Table 2. Algorithm categories.
Advantages Disadvantages
Basic Statistical
Techniques
Simple, easy to use Does not obtain enough semantic
information to make accurate
predictions about entailment vs
contradiction
Neural Networks Incredibly effective, achieves
state-of-the-art results
Incredibly computationally expensive
and memory intensive, difficult to
debug
Knowledge/Corpus-
Based Measures
Can trivially obtain and
process the semantic
information needed for
accurate predictions
Relies heavily on large corpora and
semantic networks to work properly, is
designed for word/concept similarity
and thus difficult to scale
Yigit Sever and GonencErcan perform a comparison of cross-lingual semantic similarity methods
by building a cross-lingual textual similarity evaluation dataset utilizing seven different
languages [5]. Wordnets measure concepts, and while some concepts are specific to a given
language, others are shared between languages. By linking several of these synsets together, a
foundation is built for building the evaluation dataset. The resulting dataset can determine the
effectiveness of both unsupervised and supervised text similarity algorithms, allowing the two
categories to be easily compared. Word embeddings are victim to the hubness problem, where
many of the vector embeddings are similar to other vector embeddings. To investigate how hubs
such as these can affect the similarity result, the evaluation task is split into two different tasks:
alignment and pseudo-retrieval.

44
In addition to building the evaluation dataset, the paper also compares several text similarity
models, both unsupervised and supervised. Among the algorithms tested is machine translation
the monolingual baseline (MT), cosine similarity between sentence embeddings (SEMB), word
mover’s distance (WMD), Winkhorn (SNK), and Siamese long-short term memory (LSTM).
Concerning retrieval, WMD surprisingly performed the best, followed by SNK. LSTM
performed the third best, but it was noted that it was trained using a limited number of instances,
so it may be possible for it to score higher in this area. Concerning the matching task, SNK
performed the best, followed by WMD, followed by SEMB.
Yuki Arase and Junichi Tsuji developed a method of improving the BERT model through
transfer fine tuning [6]. Their method of pretraining focused on semantic pair modeling,
allowing the proposed method to have significant improvements over the normal BERT model.
Furthermore, in addition to performing better than the baseline BERT model, it is also more cost-
effective, as it focuses on phrase alignments, which can be automatically generated.
As part of the evaluation process, the paper used two different benchmarks: the GLUE
Benchmark, and the PAWS dataset. The GLUE Benchmark used consists of nine different
evaluation datasets whose tasks cover various parts of natural language understanding. The most
relevant tasks for the paper, however, is that of Semantic Equivalence, whose subtasks consist of
paraphrase understanding and an understanding of Semantic Text Similarity (STS). The PAWS
dataset, like the MRPC dataset, focuses on paraphrase understanding, but utilizes controlled word
swapping and back translation to determine if an algorithm is sensitive to context and word order.
In the GLUE Benchmark, the proposed method successfully performed better than their BERT
counterpart in all areas except for QNLI and SST, while in the PAWS benchmark the proposed
method performed better than BERT in all areas. Furthermore, the evaluation showed that the
proposed method performed better when the fine-tuned training corpus used is smaller, which
would make creating said training corpi much easier.
3. PROPOSED METHODOLOGY
Statistical techniques are the simplest of the three types of document similarity algorithms.
They compare text by first turning the sentences into vectors, and then comparing said vectors.
The most used and most effective way of comparison is through cosine similarity, but other
methods such as Euclidean distance are occasionally used. Of the possible preprocessors,
Sentence-BERT was judged to be most ideal for the purposes of this paper. This is because it
performed incredibly well among the algorithms tested for this purpose and was the second most
computationally efficient algorithm tested [7]. Neural networks are another possible avenue of
document similarity, and a very effective one. The basics of neural network-based techniques is
that they are first fed training documents of pairs of texts that are either similar to each other or
different. Gradually, the neural network learns which pairs of texts are similar and which pairs
are different through understanding semantic information. Generally, the text is run through a
tokenizer first, which allows the semantic information to be more understandable to the network.
Some models, like BERT and XLNet, are pre-trained, which allows the network to have
accurate predictions without needing long training times and massive corpora for each
individual task. Figure 1 shows a typical methodology of document analysis using statistical
algorithms.

45
Figure 1. Methodology of statistical algorithms.
Corpus-based measures find text similarity by using large corpora to determine the similarity
between different words [8]. Two methods that utilize this technique are Pointwise Mutual
Information, and Latent Semantic Analysis. Pointwise Mutual Information accomplishes this by
determining how often the compared words appear together, while Latent Semantic Analysis
accomplishes this by performing singular value decomposition on the corpus. Knowledge-based
measures follow a similar path by converting words into concepts, and then using semantic
networks to compare those concepts [8].
Two neural-network systems are included, as they seem to be overall most effective. These are
knowledge-distilled MT-DNN, and XLNet [9]. The Multi-Task Deep Neural Network proposed
in [10] is improved using knowledge distillation. Several “teacher” MT-DNNs are developed,
one for each task. Each “teacher” MT-DNN then generates a set of “soft” targets, which are
combined with the correct “hard” targets for their respective task. A single “student” MT-DNN
then uses these combined targets as the target for each task. The end result of this process is that
the “student” MT-DNN is able to significantly outperform other MT-DNNs that do not use
knowledge distillation as part of their training. XLNet combines the usage of both
autoregressive language modeling and autoencoding to build a pretrained model that can obtain
context from both previous and future words without relying on data corruption like BERT [11].
As a result, XLNet is able to significantly outperform the baseline BERT in most NLP tasks. In
addition, a statistical technique is also included, dubbed Sentence-BERT + cosine similarity.
Sentence-BERT is a modification of BERT that uses siamese triplet networks to derive sentence
embeddings that are both semantically meaningful and can easily be compared using cosine
similarity [7]. In addition to these algorithms, a combination of the Lin knowledge-based
measure proposed in [8] and a string similarity method proposed in [12] and [4] is also used.
The Lin knowledge-based measure is designed specifically to understand semantic similarity,
while the string similarity method was designed to understand lexical similarity. As a result, the
combination of the two algorithms would be able to understand far more information than each
of the algorithms individually.
The datasets used will be the Microsoft Research Paraphrase Corpus (MRPC) [13], the
Argument Facet Similarity (AFS) dataset [14], and the Sentences Involving Composition
Knowledge (SICK) dataset [15]. The MRPC dataset is designed to test whether the algorithm
can detect paraphrases, while the AFS dataset is designed to test whether the algorithm can
detect similar arguments. The SICK dataset is split into two tasks, named SICK-R and SICK-E.
SICK-R tests the algorithm on the sentences’ lexical similarity, while SICK-E tests whether the
algorithm is capable of high-level semantic knowledge, by deciding whether the sentences are
similar, have no relation, or have the opposite meaning. The evaluations used for the tasks will
be the Pearson correlation and Spearman rank correlation for AFS and SICK-R, and
classification accuracy for MRPC and SICK-E.

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4. RESULTS
Overall, the implementation of Sentence-BERT + cosine similarity went well. For the most part,
the corpuses, once downloaded, were trivial to implement. The biggest issue came from the
AFS dataset, as it was stored in a comma separated value format, which made it difficult to
distinguish the commas in sentences from the commas separating the data. This issue was
solved by editing the dataset to make every comma in a sentence appear twice, and then use
regex to split the sentences using only the single commas from the CSV format. Once the
sentences were split, the double commas could be converted to single commas trivially. The
evaluations were somewhat difficult to code, but once their respective formulas were found
implementing them was relatively trivial. Unfortunately, due to how SICK-E’s classification
works, SemanticBERT + cosine similarity is unable to properly classify the information, and a
score could not be found.
Unfortunately, the implementation of XLNet was not so simple. The original plan was to use
the implementation linked in the original paper [16], but after much consideration the plan was
changed to use hugging face transformers [17] instead, as it would be significantly easier to
import and use. Initially, there were major issues with the XLNet model, as the input
dimensions would consistently be off. This was solved through usage of padding. Then,
training and evaluation became an issue, as running either would consistently fail from using too
much GPU memory. This was solved by reducing batch steps. After this, I was able to find the
accuracy of classification datasets without issue, but unfortunately, finding an XLNet model
capable of regression was still a major problem.
Implementing MT-DNN was a major issue from the very beginning. It took several days to
figure out atensorflow import that kept going wrong required an older version of Python (3.6),
and another day after that to realize the GitHub project used [18] was designed for a Linux OS.
After those two problems were found, the location of implementation was switched to a cloud
computing server known as Paperspace Gradient. Once accomplished, the model was able to be
set up. Import errors made it difficult at times but installing them via apt install worked nicely to
clear up the problems. Rewriting code in the program was often necessary to account for
running python programs with python3 instead of python2, as well. Furthermore, for the MT-
DNN to work quickly, reinstalling a CUDA driver that was only partially installed became
necessary, which was difficult as simply deleting it did not work as there were dependencies
involved. Ultimately, manually deleting the system programs that relied on said driver, then
deleting the driver solved the issue. Uploading the datasets seemed simple at first, but quickly
became difficult, as each task had a specific data orientation that the uploaded datasets did not
match. After preprocessing the datasets again to account for this and splitting them up into
train/test/dev splits, evaluation was thankfully simple.
Implementing the knowledge-based measure was for the most part simple, although there were
some hiccups along the way. The initial plan was to use the implementation mentioned in [8],
but it was not linked anywhere in the paper and it could not be found online. Ultimately, the
implementation was changed to include Sematch [19,20] as the implementation for the
knowledge-based measure. Originally, the plan was to combine multiple knowledge-based
measures together, but it quickly became clear that doing so would be difficult to impossible in
the time remaining. Ultimately, the knowledge-based measure chosen was the Lin method, as it
seemed the most effective of the individual algorithms mentioned in [8] and had an easy
normalization of between 0 and 1. Having chosen the algorithm and GitHub implementation, the
most difficult problem of implementation made itself known. Knowledge-based measures work
by turning words into concepts and comparing those concepts, and thus they are designed for
word similarity instead of sentence similarity. All the evaluation metrics compared sentence

47
similarity, so it was necessary to convert word similarity to sentence similarity. Thankfully,
several papers that did just that were found [12,13]. They achieved this feat by building a matrix
of comparisons between each word in the two sentences. Once this was done, the papers took the
highest value in the matrix, added it to a list, deleted the row and column the value was in, and
continued until the highest value was zero or until there were no columns or rows left. The
authors also combined the knowledge-based measure with a similarity measure so that the
algorithm would be more sensitive to lexical information. It was trivial to use those papers to
convert the implementations’ word similarity to sentence similarity, and from there things
proceeded smoothly.
Table 3. Result analysis per task/evaluation combination.
SICK-R
Pearson
SICK-R
Spearman
SICK-E
Accuracy
AFS
Pearson
AFS
Spearman
MRPC
Accuracy
Sentence-BERT
+ Cosine
Similarity
73.61% 75.038% N/A 28.034% 75.063% 68.017%
MT-DNN 92.185% 88.567% 90.706% 80.860% 78.178% 88.286%
XLNet2 (second
implementation)
47.697% N/A 57.383% 72.752% N/A 66.236%
XLNet1 (first
implementation)
N/A N/A 56.911% N/A N/A 66.983%
Lin Measure +
String Similarity
60.546% 75.038% N/A 32.273% 75.063% 70.172%
During the XLNet implementation, it became clear that the transformers XLNet code being
working with was designed for classification problems and could thus not handle continuous
data. As such, it was decided to implement a different implementation linked in the original
XLNet paper [16] as well to ensure that XLNet data concerning SICK-R and AFS could be
gathered. The main issue was the matter of installing CUDA 10.0, as Paperspace Gradient
automatically installed CUDA 10.2, which needed to be uninstalled before CUDA 10.0 was
installed. There were also issues with the Nvidia driver, which would prevent CUDA from fully
installing. In the end, the issue was resolved by removing all traces of CUDA from my system,
partially installing CUDA 10.0, manually removing the nVidia driver and its dependencies, and
then running apt --fix-broken install to properly install CUDA 10.0. After this, implementation
largely went smoothly. The only real roadblocks after this point was that XLNet only used
Pearson similarity as a metric, and it did not naturally include MRPC processing. Coding in an
MRPC Processor subclass using their basic MNLI Matched Processor became necessary, after
which the evaluation went smoothly. Unfortunately, adding Spearman Rank Similarity was a
much more arduous task, and there was no time left to finish the job. All results used 60% train,
20% test and 20% dev datasets. Figure 2 shows the accuracy of document similarity algorithms.

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Figure 2. Result analysis per task/evaluation combination.
Both Sentence-Bert + cosine similarity and the unofficial XLNet code was implemented on an
Alienware m15 R4 with an Intel i7-10870H CPU, 32 GB of RAM, and a NVIDIA GForce RTX
3070 GPU. The MT-DNN, second XLNet implementation and Lin measure + string similarity
was implemented on Paperspace, with an Intel Xeon E5-2623 v4 CPU, 30 GB of RAM, and a
NVIDIA P5000 GPU.
Of the algorithms tested, the MT-DNN is by far the most effective, as it was the only algorithm to
successfully process all evaluation metrics, and it also had the highest scores in all evaluation
metrics. Lin Measure + String Similarity and Semantic BERT + Cosine Similarity had roughly
the same results, with Semantic BERT + Cosine Similarity performing better in the SICK-R
Pearson task, but slightly worse in the AFS Pearson and MRPC tasks. The official XLNet code
performed incredibly well in the AFS Pearson task but performed badly in the SICK-R Pearson
task and had a slightly worse MRPC score than Lin + String Similarity and BERT + cosine
similarity’s scores. However, unlike Lin + String similarity and BERT + Cosine Similarity, the
second XLNet implementation was able to give a score for SICK-E, so it has a major advantage
in flexibility. The first XLNet implementation had similar scores to the second XLNet
implementation, but was unable to handle continuous data, so in terms of versatility and results it
performed the worst of all tested algorithms.
In the future, XLNet could be further investigated. The second implementation of XLNet is
theoretically capable of using Spearman Rank similarity as a metric, making it the only algorithm
other than MT-DNN that can process all evaluation metrics.
5. CONCLUSIONS
This research gave a comprehensive study of four algorithms, Semantic BERT + Cosine
Similarity, MT-DNN, XLNet, and Lin Measure + String Similarity. Using the MRPC, AFS,
SICK-E and SICK-R datasets, and the Spearman Rank, Pearson, and Accuracy evaluation
metrics, the research was able to look at each of the four algorithms strengths and weaknesses,
and thus give an answer to the question of which document similarity algorithm is most effective.

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From the data obtained in this research, the MT-DNN is clearly the most effective document
similarity algorithm tested.
ACRONYMS
MRPC: Microsoft Research Paraphrase Corpus.
AFS: Argument Facet Similarity.
SICK: Sentences Involving Composition Knowledge.
BERT: Bidirectional Encoder Representations from Transformers.
MT-DNN: Multi-Task Deep Neural Network.
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A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMS

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