Legal Information Retrieval Based on a Concept-Frequency
Representation and Thesaurus
Wagner Miranda Costa and Glauco Vitor Pedrosa
a
Graduate Program in Applied Computing (PPCA), University of Brasilia (UnB), Brasilia, Brazil
Keywords:
Information Retrieval, Data Mining, Natural Language Processing, Deep Learning, Decision Support System.
Abstract:
The retrieval of legal information has become one of the main topics in the legal domain, which is characterized
by a huge amount of digital documents with a peculiar language. This paper presents a novel approach, called
BoLC-Th (Bag of Legal Concepts Based on Thesaurus), to represent legal texts based on the Bag-of-Concept
(BoC) approach. The novel contribution of the BoLC-Th is to generate weighted histograms of concepts
defined from the distance of the word to its respective similar term within a thesaurus. This approach allows
to emphasize those words that have more significance for the context, thus generating more discriminative
vectors. We performed experimental evaluations by comparing the proposed approach with the traditional Bag-
of-Words (BoW), TF-IDF and BoC approaches, which are popular techniques for document representation.
The proposed method obtained the best result among the evaluated techniques for retrieving judgments and
jurisprudence documents. The BoLC-Th increased the mAP (mean Average Precision) compared to the
traditional BoC approach, while being faster than the traditional BoW and TF-IDF representations. The
proposed approach contributes to enrich a domain area with peculiar characteristics, providing a resource for
retrieving textual information more accurately and quickly than other techniques based on natural language
processing.
1 INTRODUCTION
The legal domain is characterized by a huge
amount of digital documents, having a peculiar
language and implicit structures. The large amount
of digital has currently pointed out the need of
more complex strategies for the management of
the embedded unstructured knowledge in terms
of analysis and extraction of relevant information.
According to (Niebla Zatarain, 2018), computational
processes changed the practice of law, specifically by
connecting computational models of legal reasoning
directly with legal text, generating arguments for and
against particular outcomes.
An important task in the legal domain is the
retrieval of similar information from a large and
diverse dataset. This is a task that can be used
to give access to the law to lay people and legal
professionals, which increases the transparency and
legitimacy of the processes (Sansone and Sperl
´
ı,
2022). For example, using legal information retrieval
it is possible to monitor changes in tax laws and
a
https://orcid.org/0000-0001-5573-6830
regulations or use Legal Reasoning in order to check
compliance and/or validate the quality of legislative
documents.
A fundamental and crucial step for the deve-
lopment of a textual information retrieval system
is to extract a compact and discriminative vector
representation of the documents. The Bag-of-Words
(BoW) model, for example, is one of the most
popular techniques for document representation
and it is used in many text retrieval applications
(Salim and Mustafa, 2022). This approach counts the
word frequencies of a document and allows intuitive
interpretability of the feature vector, but suffers
from the curse of dimensionality and disregards the
impact of semantically similar words. To increase the
semantic representation of documents, we can encode
words by using Word Embeddings techniques, such
as the popular word2vec model (Le and Mikolov,
2014). This approach is based on deep neural
networks and it encodes the contextual information
of each word by capturing information around it.
However, the vectors generated from word2vec are
difficult to interpret as its values indicate the weight
of the neural network used for training.
Costa, W. and Pedrosa, G.
Legal Information Retrieval Based on a Concept-Frequency Representation and Thesaurus.
DOI: 10.5220/0011728400003467
In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 1, pages 303-311
ISBN: 978-989-758-648-4; ISSN: 2184-4992
Copyright
c
 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
303

To overcome the drawbacks of the BoW and
Word Embeddings techniques, the Bag-of-Concepts
(BoC) model (Kim et al., 2017) emerged as an
alternative approach for document representation by
clustering semantically similar words into a common
“concept”. Concepts are created by clustering
word vectors generated from Word Embedding
techniques, such as word2vec (Mikolov et al.,
2013) or wang2vec (Ling et al., 2015). Using the
idea of “concepts”, the BoC allows to encode the
impact of semantically similar words, enriching
the representation of a textual document without
increasing the dimensionality of the feature vector
while offering intuitive interpretability behind the
generated document vectors.
In this paper, we introduce the BoLC-Th (Bag
of Legal Concepts Based on Thesaurus) to represent
legal documents, complying with the vocabulary of
legal texts in the BoC representation. The goal of the
proposed approach is to emphasize those words/terms
that are directly related to the peculiar language of
legal documents. For this purpose, the BoLC-Th
uses a thesaurus, which is a type of controlled
vocabulary whose relationships between its terms are
identified through standardized indicators and which
are mutually employed. In the proposed approach,
each word will have different weight in the final
document representation. This weight is based on
the distance from the word to the closest term in the
thesaurus: the greater the distance the less the weight.
In this way, it is possible to represent documents in a
more semantically effective way, complying with the
domain context.
We evaluated and compared the proposed method
with other text representation approaches using
a case study with real data. We used a database
of jurisprudence texts from the Federal Court of
Accounts (TCU) from Brazil, which is the Brazilian
federal accountability office responsible for federal
public funds as well as the accounts of any person
that causes loss to the public treasur. With the help
of experts from TCU, we carried out experiments to
retrieve similar jurisprudence to a given judgment
provided by the users (auditors).
This paper is structured as follows: Section
2 presents related works; Section 3 presents the
steps of the proposed method; Section 4 shows
the results obtained from an empirical experiment
carried out with real data; and Section 5 presents our
conclusions.
2 RELATED WORKS
The information retrieval deals with the development
of algorithms and models to retrieve information
from a large and diverse document repository (Yan,
2009). From a computational point of view, for the
implementation of an information retrieval system,
first the documents must be represented in a way that
the computer can interpret each document within the
collection. This representation aims to numerically
represent unstructured text documents to make them
mathematically computable. In other words, for a
given set of text documents D = {d
1
, d
2
, d
3
, ..., d
n
},
where each d
i
represents a document, the textual
representation problem is to represent each d
i
of D
as a point s
i
in a feature space S, where the distance
between each pair of points in the feature space S is
well defined.
To transform textual data into a numerical vector,
it is necessary to use a document representation,
which can be divided into two categories: word
frequency-based and embedding prediction-based
(Analytics Vidhya, 2017). In the first group, the
document vectors is based on counting words, in the
second group, the generation of vectors is supported
by neural network mechanisms, which result in
non-deterministic numerical representations. Figure
1 shows how these techniques are used in this study.
In the following, we discuss the concepts and the
related works that motivated the development of this
work.
2.1 Bag-of-Words (BoW)
Representation
A commonly adopted and effective approach to
document representation is the Bag-of-Words
model. The BoW model assigns a vector d =
{x
1
, x
2
, x
3
, ..., x
l
} to a document d, where x
i
denotes
the normalized number of occurrences of the i-th
term (word) in the document, and l is the size of the
collection of terms (words) of documents present in
the database.
The BoW approach is a simple but effective
method for mapping a document to a fixed-length
vector. However, the mapping function in the BoW
model is hard (or binary), as it only represents the
presence or not of a term (word) in the document.
The hard mapping function has several limitations:
first, the vector generated for each document is
extremely sparse, as a document contains only a very
small portion of all the basic terms in a database.
Second, BoW representations may not effectively
capture the semantics of documents, as semantically
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Figure 1: Overview of different modeling-based approaches for textual document representation. The blue lines indicates
the Bag-of-Words model, the red line indicates the word embedding techniques and the green lines the Bag-of-Concept
representation.
similar documents with different sets of words will be
mapped to very different vector spaces. To overcome
these drawbacks, we can use word embedding
technique, that will be discussed in the following.
2.2 Word Embeddings Techniques
It is natural to look for codings that account for
semantic relationships between words (Turney,
2006). This leads to the creation of a so-called
word embedding (also named as “semantic vector
space” or simply “word space”), i.e., a continuous
vector space in which the relationships among the
vectors is somehow related to the semantic similarity
of the words they represent. The goal of word
embedding is to capture semantic and syntactic
regularities in language from large unsupervised
sets of documents, such as Wikipedia. Words that
occur in the same context are represented by vectors
in close proximity to each other. Fig. 2 shows an
example of such embedded space: in this figure, we
have embedded words that represent the names of
sports car, companies and fruits. While the words
with similar meanings are located closer to each
other, the words with different meanings are located
distant from each other.
The central idea behind word embedding
techniques is to assign a vector representation to each
word, so that words that are semantically similar are
close to each other in vector space. The merit of
the word embedding approaches is that the semantic
similarity between two words can be conveniently
evaluated based on the measure of cosine similarity
between the corresponding vector representations of
the two words.
One of the most popular word embedding
technique is the word2vec (Mikolov et al., 2013),
which is based on a two-layer neural network. The
word2vec framework contains two separate models,
Figure 2: Example of a clustered embedded space.
Legal Information Retrieval Based on a Concept-Frequency Representation and Thesaurus
305

Figure 3: Overall framework of Bag-of-Concepts model.
including Continuous Bag-of-Words (CBoW) and
Skip-gram with two reverse training objectives.
CBoW tries to predict a word by considering the
words around it, while Skip-gram tries to predict a
window of words given a single word. Due to its
architecture and unsupervised training, word2vec
can be efficiently built on a large-scale unannotated
corpus. Word2vec is able to encode meaningful
linguistic relationships between words in embeddings
of learned words.
Embedding at document level is used in (Renjit
and Idicula, 2019) for the similarity assessment of
legal texts. Using the paragraph vector (Le and
Mikolov, 2014) algorithm, the authors consolidate
the text feature vectors and estimate the remaining
paragraphs with the stochastic gradient descent
method. Then, the angular orientation of the
documents is obtained, from the calculation of
the cosine similarity of their vectors. Documents
that have similar orientations are also considered
semantically similar.
2.3 Bag-of-Concepts (BoC)
Representation
In the Bag-of-Concepts approach, the words of a text
are associated with their semantic representations,
obtained by clustering the embedding space of its
words. This grouping will generate a codebook of
concepts that will be used to represent the textual
documents. Figure 3 shows the overall framework of
this model representation, which is divided into two
steps: coding and pooling. In the coding step, the
words are numerically represented using some word
embedding technique and, then, using a codebook
of contexts, each word is assigned to a context. The
pooling step consist of counting the frequency of
each concept within the document.
The Bag-of-Concepts approach overcome some
limitations of the BoW approach, such as the lack
of semantic representation, high dimensionality and
sparseness (Kim et al., 2017). The definition of
“concept” as a unit of meaning was presented by
(Mourino Garcia et al., 2015), who studied the use
of knowledge databases based on the Wikipedia. As
a result, a gain of up to 157% was obtained in the
modeling of classifiers when compared to BoW.
2.3.1 Extended Models for Bag-of-Concepts
The BoC technique has been used, and sometimes
extented, in order to improve the representation
of textual document for classification and/or
retrieval tasks. The work of (Li et al., 2020), for
example, proposes the Bag-of-Concepts-Clusters
(BoCCl) model, which groups semantically similar
concepts and enhances the BoC representation
with entity disambiguation. The problem of
synonymy and polysemy is considered in (Wang
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et al., 2014), which highlights the disadvantage in
vector analysis of short texts, since even texts with
similar meanings may not share the same words.
(Shalaby and Zadrozny, 2019) uses knowledge
bases (KB), such as Wikipedia and Probase, to
support the conceptualization. Additionally, neural
representations support the densification of BoC
vectors to decrease their sparsity without, however,
increasing their dimension. The authors indicate a
1.6% improvement in the correlation score and a 5%
reduction in categorization errors, and they evaluated
the proposed model from three perspectives:
1. Semantic relationship between entities, comparing
it to Wikipedia Link-based Measure (WLM),
Keyphrase Overlap RElatedness (KORE),
Exclusivity-based Relatedness (ExRel) and
Combined Information Content (CombIC)
models
2. conceptual categorization, comparing it to word
embeddings trained on Wikipedia, multiword
embeddings trained on Wikipedia, and entity-
category embeddings models
3. unsupervised classification of documents against
Explicit Semantic Analysis (ESA), Word
embeddings best match and Word Embeddings
Hungarian algorithm
Instead of comparing documents individually,
(Hematialam et al., 2021) seeks to find patterns of
similarity between sets of documents of the same
nature, such as between full texts and respective
summaries, by analyzing the distortion of the
graphs resulting from the calculation of the cosine
distances of the embeddings vectors. This approach
allows texts of different sizes and feature vectors of
different dimensions to be semantically compared,
with satisfactory results. A greater consistency
in the semantic interpretability of the features of
the texts is found in (Li et al., 2020), which in
addition to grouping the concepts, uses probabilistic
knowledge base (ProBase) and lexicon (WordNet) for
disambiguation of entities. Something similar is done
by (Rajabi et al., 2020), but in the disambiguation of
the grouped concepts themselves.
A characterization method supported by domain-
specific ontology (OCTC – ontology-enabled
concept-based text categorization) is presented
by (Lee et al., 2021). Here, a syntactically marked
document is converted into a conceptual representation
according to the frequency of descriptors of existing
concepts in the ontology. This frequency defines
the degree of relevance of the concept in a given
document. The study states that the conceptual
representation requires less computational resources
in its generation and that the accuracy of the OCTC
classifier is superior to that of other techniques, such
as Latent Semantic Analysis (LSA) and Doc2Vec
combined with Support Vector Machine (SVM).
The study in (Lee, 2020) works on the treatment
of homonymy by replacing the word embedding of
the homonyms with their semantic meaning, from
the grouping of Embeddings from Language Models
(ELMo). Locating semantic relationships between
terms – hyperonymy and meronymy, for example –
was also the approach of (Mehanna and Mahmuddin,
2021), albeit in the context of sentiment analysis.
With the use of semantic databases to mark aspects
in BoC of texts, this method reached an increase of
up to 70% in the accuracy when performing the same
task through algorithms based on networks neural
and Naive Bayes.
3 PROPOSED METHOD
Towards an effective representation of documents
in the legal domain, in this paper we introduce
a novel textual-based representation technique,
called BoLC-Th (Bag of Legal Concepts Based on
Thesaurus). The goal of the proposed method is to
represent textual documents in weighted histograms
of concepts. This histogram is the textual document
representation that can be used to retrieve similar
content-based documents. The BoLC-Th employs
a vocabulary of words used in the analysis of legal
documents to increase the discriminative power of the
document representation. For this purpose, the idea
of the proposed technique is to use a theasurus (which
has the terms with the words and their synonyms of
the application domain).
Figure 4 shows the steps of the proposed
approach, which is divided into two phases: (i)
coding and (ii) pooling. The coding phase assigns
a concept to each word of the textual document,
and the pooling phase summarizes the frequency of
each concept in the document. These two phases
will be detailed as follows. However, first we need
to describe the concept-based dictionary generation,
which is used to assign a concept to each word in the
document.
3.1 Concept-Based Dictionary
Generation
Let W = {w
1
, w
2
, ..., w
v
} be the vocabulary that
encodes all the words for a given corpus, and v
the size of that vocabulary. Each word w
i
∈ W
is represented by a r-dimensional vector obtained
Legal Information Retrieval Based on a Concept-Frequency Representation and Thesaurus
307

Figure 4: Steps of the proposed method to represent a document in a weighted histogram of concepts based on a thesaurus:
the blue lines indicate the generation of concepts for each words in the document and the red lines indicate the weighted of
each word according to their nearest similar word in the thesaurus. The final representation of the document is a weighted
frequency of concepts.
using any word embedding technique, for example
word2vec, generating a matrix S ∈ R
v×r
. Let
T = {t
1
, t
2
, ..., t
m
} be a thesaurus with m words,
where each t
i
corresponds to a term of a specific
domain of knowledge that, in the case of this work,
involves the words and their synonyms used in the
texts of legal documents.
To generated the concept-based dictionary,
we need to run a clustering algorithm to cluster
semantically similar words. In other words, let
C = {c
1
, c
2
, ..., c
k
} be this dictionary: each concept
c
i
∈ R
r
is the centroid of a group C
i
⊂ {S ∩ T }, such
that:
c
i
=
1
|C
i
|
∑
x
j
∈{S∩T }
x
j
, ∀i ∈ {1, 2, ..., k} (1)
where, |C
i
| is the number of words associated
with the group C
i
and k is the number of groups
(concepts). Any clustering algorithm can be used to
generate these clusters. However, the use of Spherical
k-means is indicated by (Dhillon and Modha, 2001)
for clustering sparse and high-dimensional vectors,
which is the case of the data analyzed in this work .
The proposed approach for the generation of the
dictionary of concepts differs from the traditional
approach by considering only those words from the
corpus that are present in the thesaurus. Our goal is
to restrict the generation of concepts to words strictly
related to the application context.
3.2 Weighted Pooling of Concepts
Let D = {d
1
, d
2
, ...., d
N
} be a textual document, where
N is the number of words in that document and each
word d
i
∈ D is represented by a r-dimensional vector
obtained by the same word embedding technique
used to generate the concept-based dictionary. Each
word d
i
∈ D is associated with each of the existing k
concepts in C using a function φ : R
d
→ N
k
, defined
as:
d
i
→ φ(d
i
) = {α
i
1
, α
i
2
, ..., α
i
k
} (2)
where α
i
j
can be seen as an activation function,
associating the i-th word of the document to the j-th
concept of the dictionary of concepts. This activation
can be hard or soft. Classic coding is based on hard
coding where each word d
i
is assigned to only one
concept, that is:
α
i
j
=
(
1, if j = argmin
j∈{1,...,k}
||d
i
− c
j
||
2
2
0, otherwise.
(3)
The innovative approach of this work consists of
using a weighted concept-based pooling. The idea is
to calculate the distance of each word d
i
∈ D to its
nearest similar word t
r
∈ T . In other words, the final
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vector that will represent the document D will be a
weighted histogram of concepts H = [h
1
, h
2
, ...h
k
],
where k is the number of concepts in the concept
dictionary and each h
j
∈ H is defined as:
h
j
=
N
∑
i=1
p(d
i
)α
i
j
, ∀ j ∈ {1, 2, ..., k} (4)
where,
p(d
i
) = 1 − argmin
t
r
∈T
||d
i
−t
r
||
2
2
(5)
The weighting function p : R
d
→ R returns the
distance from the word d
i
to its nearest similar word
in T . If d
i
∈ T , then p(d
i
) = 1 and, therefore, the
concept associated with the word d
i
will be fully
accounted in the histogram. Otherwise, the greater
the distance of word d
i
is from the its nearest word
in the thesaurus, the smaller the contribution of the
concept of the word d
i
in the final histogram.
4 EXPERIMENTAL RESULTS
The proposed method BoLC-Th was compared
with three other traditional approaches for textual
document representation: BoW, BoC and TF-IDF.
The performance of each technique was evaluated
on a case study, involving the retrieval of similar
judgments and jurisprudence of the Federal Court
of Accounts (TCU), which is the Brazilian federal
accountability office. It is tasked with assisting
Congress in its Constitutional incumbency to exercise
external audit over the Executive Branch.
4.1 Database and Ground-Truth
The database for carrying out the comparative
experiments is composed of a set of 15,000 (fifteen
thousand) statements of jurisprudence from the
Federal Court of Auditors (TCU), available on its
Portal
1
. In the process of preparing case law, TCU
experts group judgments according to the areas of
activity of the External Control and consolidate the
Court’s understanding of a given subject. These texts
are called statements. In turn, the judgments, which
represent the decisions of the Court, have their texts
summarized into summaries.
In order to establish a benchmark for our
comparative tests, a ground truth was built to
compare the results obtained. This base is composed
by the selection of 10 (ten) summaries of judgments
handed down by the TCU. For each of these
1
https://www.tcu.gov.br/
summaries, TCU experts manually indicated the
utterances with the greatest semantic similarity. This
set (summaries of judgments and their respective
statements) constitutes the ground truth of this work.
4.2 Thesaurus
Thesaurus can be defined as a list of terms with
their synonyms – or some other equivalent semantic
relationship. Commonly, it deals with a domain of
knowledge, which makes its use very specific. For the
scope of this work, the TCU developed a Thesaurus
with the aim of standardizing the terminology
used in the Court’s institutional activities, in
addition to supporting the handling of information
in organization. This Thesaurus is structured by
government functions and has Subject, Entity and
Locality descriptors. It is composed of 10,525
synonym-term tuples and Table 1 shows some sample
of the Thesaurus used in this work.
Table 1: Examples (in Portuguese language) of the term-
synonym list of the Thesaurus developed by TCU and used
in this work.
Term Synonym
Tributo Obrigac¸
˜
ao fiscal
Sentenc¸a Decis
˜
ao judicial
Ren
´
uncia de receita Ren
´
uncia tribut
´
aria
Governo eletr
ˆ
onico e-GOV
Patrim
ˆ
onio mobili
´
ario Bens patrimoniais m
´
oveis
Incentivo fiscal Gastos tribut
´
arios
Whitelist Remetentes confi
´
aveis
Preju
´
ızo Dano contratual
Capacitac¸
˜
ao Qualificac¸
˜
ao profissional
Racismo Discriminac¸
˜
ao racial
4.3 Parameter Settings
The generation of feature vectors and the execution
of similarity queries were performed in the Python
3.8 programming language, with support from
the scikit-learn, Gensim, Pandas, Matplotlib and
NumPy libraries. The execution environment for this
work was Jupyter Notebook 6.4.12, processed on a
computer with the following configuration: Intel(R)
Core(TM) i7-10610U CPU @1.80GHz 2.30GHz,
20GB RAM, Windows 10 Pro 64 Operating System
-bit.
For the implementation of the BoC and BoLC-Th
approaches, two parameters need to be defined:
1. the size of the concept-based dictionary
2. the size of the vector-of-words generated by the
word2vec technique.
Legal Information Retrieval Based on a Concept-Frequency Representation and Thesaurus
309

Table 2: Vector dimensionality of each technique for
representing a textual document.
Technique Vector Dimensionality
BoW 10.007
TF-IDF 10.005
BoC 100
BoLC-Th 100
Empirically, we performed exhaustive tests with
different values for these two parameters in order to
investigate which combination had the more accurate
values. The best parameters for the two approaches
were obtained with the vector-of-words (word2vec)
with a dimension equal to 100 and a dictionary with
100 concepts. Therefore, these two parameters were
used, both by the BoC and BoLC-Th approaches.
Table 2 shows the vector dimensionality used for
each technique to represent a textual document: the
BoC and the BoLC-Th techniques have the smallest
vector size. The vector size used by the BoC and
BoLC-Th approach is 99% smaller than the BoW
and TF-IDF approaches. This is reflected in storage
space savings and, mainly, in the low computational
cost to perform the calculation of the similarity
between the vectors in the process of retrieving
similar documents.
Figure 5: Best mAP and NDCG values obtained for each
evaluated techniques.
4.4 Obtained Results
The metrics used to evaluate the performance of
the techniques were: the mean Average Precision
(mAP) and Normalized Discounted Cumulative Gain
(NDCG). Both measures are often used to measure
effectiveness of web search engine algorithms or
related applications.
Figure 5 shows the best values obtained for each
evaluated technique. We can see that the BoLC-Th
and TF-IDF achieved superior values compared
to the BoC and BoW techniques. Comparing the
BoLC-Th and TF-IDF, the first one achieved superior
performance in both measures (mAP and NDCG).
One of the main drawback of information retrieval
systems is the execution time used to compare the
query document with all other documents in the
database. For this reason, it is also important to
evaluate the efficiency of each techniques. Table 3
also shows the processing time of each technique
to retrieve similar documents. The BoW and TF-
IDF techniques have the highest processing time
to retrieve similar documents as they have the
vector with the highest dimensionality among those
evaluated. Both approaches, BoC and BoLC-Th,
have similar execution times.
Table 3: Time query processing used for each technique.
Technique Time Query Processing (ms)
BoW 0.205 ± 0.15
TF-IDF 0.285 ± 0.08
BoC 0.154 ± 0.03
BoLC-Th 0.1411 ± 0.01
An advantage of using the BoLC-Th technique
is the fast execution in calculating the similarity
between the documents in the database: as it has
a compact vector, fewer calculations are needed.
This is reflected in the scalability of the proposed
technique: the search for similar documents becomes
more efficient in an area whose database has grown
considerably over time, due to the increasing
digitization of legal processes.
5 CONCLUSIONS
This work presented a new approach, called BoLC-
Th, to represent legal textual documents. The
proposed method is an extension of the traditional
BoC approach, considering a weighted frequency of
the most important concepts in the final representation
of documents according to the application domain.
The BoLC-Th technique makes use of a thesaurus
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(specialized vocabulary), with the terms/words of the
application context, which allows to semantically
enrich the BoC method.
Experimental results were carried out with
the objective of analyzing the performance of the
proposed approach in the retrieval of legal documents
by calculating the semantic similarity of their vector
representations. The proposed BoLC-Th method was
compared with the traditional BoW, TF-IDF and BoC
approaches. The proposed method achieved better
performance when compared to the BoW model and,
on average, 40% more efficient than that obtained
with the BoC. Thus, a significant advantage was
demonstrated with the use of the BoLC-Th technique
for the analyzed case study. As future work, other
clustering techniques for the generation of concepts
should be considered.
The main contribution of the BoLC-Th is that it
incorporates the advantages of the BoC approach,
such as the compact representation, while enriching
legal documents representation as it considers
specific words/terms from the context area. This
is a valuable contribution to a domain area with
peculiar characteristics, providing a valuable tool for
retrieving textual information accurately and quickly.
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