Conceptual Analysis in a Computer-Assisted Framework: Mind in Peirce
10.6092/issn.2532-8816/7305
L’analisi concettuale (AC) è una pratica molto diffusa in filosofia e in altri campi delle scienze umane. Esplorare un corpus di testi di un autore per esaminare le proprietà di un concetto è un esempio classico di AC. Recentemente, un quadro computazionale per l’AC basata sui corpus sta emergendo per rispondere alle sfide metodologiche lanciate dalla massiccia digitalizzazione dei testi. In questo quadro, un’AC è vista come un metodo di analisi del testo assistita dal computer, in cui gli algoritmi usati supportano alcune operazioni cognitive dell’AC. In quest’articolo, ci occupiamo del processo d’identificazione dei segmenti di testo che sono pertinenti all’analisi. Tuttavia, questa è una questione complessa in un quadro computazionale, poiché la relazione tra concetto e linguaggio naturale dipende da diversi fenomeni semantici, come la sinonimia, la polisemia e la modulazione contestuale. Il contributo principale di questo lavoro è di tipo metodologico, poiché esplora l’approccio computazionale all’AC. Presentiamo tre catene di trattamento che identificano dei segmenti di testo pertinenti per una CA, tenendo conto di diversi fenomeni semantici. I risultati rivelano le potenzialità di un’assistenza computazionale all’AC, determinando così la necessità di superare i limiti di queste prime sperimentazioni. Un altro contributo è il trasferimento di conoscenze dall’intelligenza artificiale verso le scienze umane.
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Conceptual Analysis in a computer-assisted framework: mind in Peirce
https://umanisticadigitale.unibo.it/article/download/7305/7765
    Umanistica Digitale - ISSN:2532-8816 - n.2, 2018
D. Pulizzotto et al. - Conceptual Analysis in a Computer-Assisted Framework: Mind in
Peirce
DOI: https://doi.org/10.6092/issn.2532-8816/7305



 Conceptual Analysis in a Computer-Assisted Framework:
                     Mind in Peirce

  Davide Pulizzotto, Jean-François Chartier, Francis Lareau, Jean-Guy Meunier, e Louis
                                      Chartrand

   LANCI, Laboratoire d’ANalyse Cognitive de l’Information, Université du Québec, Montréal
            davide.pulizzotto@gmail.com, chartier.jf@gmail.com,
  francislareau@hotmail.com, meunier.jg@gmail.com, lochartrand@gmail.com


       Abstract. Conceptual Analysis (CA) is a matter-of-course practice for philosophers
       and other scholars in the humanities. Exploring one author?s corpus of texts in order
       to discover the various properties of a concept is a classic example of CA. Recently,
       a corpus-based computational framework for CA has been emerging in response to
       the methodological challenges brought about by the massive digitization of texts. In
       this framework, CA is approached by implementing a computer-assisted text analysis
       method, within which algorithms are used to support the various cognitive operations
       involved in CA. In this article, we focus on the retrieval of relevant text segments
       for analysis. However, this is a complex issue within a computational framework,
       since the relation between concept and natural language depends on several seman-
       tic phenomena, including synonymy, polysemy, and contextualmodulation. The main
       contribution of this article is methodological because it explores the computational
       approach to CA. We present three algorithmic methods, which identify relevant text
       segments while taking into account various semantic phenomena. The results show
       the potential of computer-assisted CA, thereby highlighting the need to overcome
       the limitations of these first experiments. An additional contribution of this work
       takes the form of knowledge transfer from Artificial Intelligence to the Humanities.

       Abstract. L’analisi concettuale (AC) è una pratica molto diffusa in filosofia e in altri
       campi delle scienze umane. Esplorare un corpus di testi di un autore per esaminare
       le proprietà di un concetto è un esempio classico di AC. Recentemente, un quadro
       computazionale per l’AC basata sui corpora sta emergendo per rispondere alle sfide
       metodologiche lanciate dalla massiccia digitalizzazione dei testi. In questo quadro,
       un’AC è vista come un metodo di analisi del testo assistita dal computer, in cui gli
       algoritmi usati supportano alcune operazioni cognitive dell’AC. In quest’articolo, ci
       occupiamo del processo d’identificazione dei segmenti di testo che sono pertinenti
       all’analisi. Tuttavia, questa è una questione complessa in un quadro computazionale,
       poiché la relazione tra concetto e linguaggio naturale dipende da diversi fenomeni
       semantici, come la sinonimia, la polisemia e la modulazione contestuale. Il contributo
       principale di questo lavoro è di tipo metodologico, poiché esplora l’approccio com-
       putazionale all’AC. Presentiamo tre catene di trattamento che identificano dei seg-
       menti di testo pertinenti per una AC, tenendo conto di diversi fenomeni semantici.


                                                185
   D. Pulizzotto et al. - Conceptual Analysis in a Computer-Assisted Framework: Mind in
                                                                                 Peirce
        I risultati rivelano le potenzialità di un’assistenza computazionale all’AC, determi-
        nando così la necessità di superare i limiti di queste prime sperimentazioni. Un altro
        contributo è il trasferimento di conoscenze dall’Intelligenza Artificiale verso le scienze
        umane.


Keywords: Conceptual analysis, Peirce, machine learning, mind, philosophy, text mining


1 Introduction

1.1 Conceptual Analysis

If Conceptual Analysis (CA) is a traditional method of inquiry in philosophy [34]; [4],
it is also used in many other disciplines. For instance, it is applied in psychiatry [36], in
psychology [72], in political science [28], in pedagogy [31], and in many other fields of the
humanities and of the social sciences. It is also a common professional practice for lawyers,
journalists, physicians, etc.
     In the humanities and especially in philosophy, one of the most common forms of CA
consists in exploring a corpus of texts by a single or by multiple authors in order to discover
the various dimensions or properties of a complex mental process, that is, conceptualiza-
tion, whose result is called a concept. In natural languages, a concept is often conveyed by
a conceptual expression, which can have various linguistic forms. The study of the concep-
tual expression of evolution and its properties in Darwin’s work, or the concept of beauty
in French novels of the nineteenth century, are examples of CA. Thus, the first step of a
CA is to find the text segments that are relevant for the analysis of the concept studied, and
this, by finding all the pertinent conceptual expressions. However, it remains difficult to
establish a widely recognized and employed CA methodology in the humanities, especially
because of the multiple and divergent theories of concepts [38]. Therefore, several methods
and approaches exist for conducting a CA [3].


1.2 Conceptual Expression and Conceptual Content

What is common to many of these approaches is the close relation between concepts and the
meaning of words. In fact, debates regarding theories of concepts have focused on the lexical
concept, that is, a concept that “correspond[s] to a lexical item in natural languages” ([43],
4). This focus finds its explanation in the assumption that the meaning of a word always
conveys a concept and that a concept always has its lexical expression. This phenomenon is
here called the standard lexical form of a concept, that is, a lexical form regularly used to express
a concept. This approach, however, limits the understanding of the problem, as there may
exist concepts that do not have such a clear correspondence in natural language. In fact, the
relation between concepts and the meaning of words is much more complex ([47], 385) and
it cannot be asserted that a concept is expressed in exclusive terms by a single lexical form
([47], 390). Often, concepts go beyond a simple lexical form and they may be expressed by
a group of words, by a definition, or by some loose thread that emerges from different text
segments. For example, the concept of marriage may well be expressed by the word itself,
or by the definition “legal union between two people” or by different text segments that


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define certain properties of marriage, such as communion of property. These latter examples
give rise to lexical expressions and semantic content that do not require the presence of a
sort of standard lexical form of the concept of marriage. In conclusion, we do not take sides
here regarding what the nature of concepts are or whether concepts are independent from
or dependent on a lexical expression. The focus of this article is on the conceptual expressions
(words or sentences) that are carriers of conceptual content, which is the set of properties or
dimensions of a concept. For simplicity, we will continue to use the term “concept” to refer
to the “conceptual content.” Ultimately, in our corpus-based approach, the standard lexical
form is a starting point from which to study a concept.



1.3 Corpus-Based and Computer-Assisted CA


In a corpus-based CA, we first have to find text segments that are relevant for the analysis.
This means considering the set of all possible relevant text segments of a corpus. When one
studies a large corpus, this becomes a more difficult and complex operation. However, new
text analysis tools can be used and adapted to the CA. In recent decades, some disciplines
such as Text Mining (TM), Natural Language Processing (NLP), Information Retrieval
(IR), and Machine Learning (ML), whose development is linked to Artificial Intelligence
(AI) [22]; [59], are inviting certain fields of the humanities into a methodological revolu-
tion. Nonetheless, this process is still in a phase of uncertainty, as the transfer of knowledge
from subfields of artificial intelligence to the humanities is still in progress.



1.4 Objectives of the Article


As we will see in the next section, various semantic phenomena are involved in the defi-
nition of the relation between concepts and natural language. In a corpus-based CA, such
phenomena must be taken into consideration. In this article, we propose three processing
chains that provide assistance in identifying relevant text segments for conceptual analysis,
while paying particular attention to the following semantic phenomena: synonymy, polysemy,
and ellipsis. We will thus present three experiments and the corresponding results obtained
with a philosophical textual corpus: The Collected Papers of C. S. Peirce. We will also show
how some elements used to identify relevant segments provide more general solutions for
CA. The main contribution of the article is the bidirectional transfer of knowledge between
AI subfields and the humanities. While philosophy needs tools to assist CA, on the other
hand, computer science uncovers original issues through the approaches to text it develops.
    The article is divided into four main sections: 1) Related Work, mostly pertaining to
works focusing on the computer-assisted approach in philosophy and on the corpus-based
CA method, 2) Problem and Theoretical Framework, in which the problem is presented with
respect to the concept vs natural language relation and where the theoretical framework sup-
porting our experiments is briefly summarized, 3) Method and Experimentation, where we
present our three experiments, 4) Conclusions, where we discuss the results and limitations
of the experiments.


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2 Related Work

The use of artificial intelligence techniques in philosophy has been the subject of explo-
ration for several years. Bynum and Moor [10] have integrated these techniques into a
general method of text analysis. Lawrence et al. [39] have used topical models to extract ar-
guments in 19th -century philosophical texts. Girju and Moldovan [30] have used ML and
NLP tools to answer philosophical questions such as the expression of causal relations in
texts. Schwartz et al. [65] have used similar techniques to explore the subjective perception
of time. Another category of work applies IR techniques for the creation of a “dynamic
ontology” for philosophy [8]. Others have explored NLP techniques to visualize semantic
networks for philosophers [1]. Some projects exploring automatic recommendation tech-
nology, in order to suggest documents (SalVe2) or providing digital editions of large corpora
(Corpus Thomisticum), have also been created.1
     Computer-assisted text analysis has also been applied for CA [45]. The first application
of such methods was probably with Allard et al. [2], in which a computer-assisted con-
cept analysis of the Quran was provided. McKinnon [44] studied the concept of destiny in
Kierkegaard’s works. More recently, other researches have explored the concept of language
in Bergson’s work [25]; [15], the concept of evolution in Darwin’s work [61], the concept
of mind in Peirce [46], the concept of management in the works of philosopher Matsushita
[18], and the concepts of mind and body in early China [67]. The majority of these works
used concordance (or KWIC) [51] for such a type of conceptual analysis. A concordance
is the set of all the text segments of a corpus where a keyword appears. In CA, the key-
word of a concordance is usually the standard lexical form of the concept under study. To
our knowledge, except for our previous works [53]; [12], all computer-assisted CAs have
been accomplished by retrieving relevant text segments only using the concordance tech-
nique. That is to say that CA has been limited to the analysis of the standard lexical form of
the studied concept. Thus, it does not go beyond work dealing with the complex relation
between concept and natural language.


3 Problem and Theoretical Framework

Despite its wide use, and perhaps because of it, CA does not have a unique definition.
As it has been introduced, CA methods depend on the nature of the concept. Theories of
concepts are categorized in many ways and it is not possible to summarize them here.


3.1 Concepts vs Natural Language

When we adopt the linguistic paradigm and the idea that natural language is necessary to
express and understand concepts [7]; [16]; [19], language becomes the privileged locus for
studying it. The use of linguistic material for philosophical purposes and, more specifically,
for CA, remains a widespread practice. However, there is no precise methodology and many
elements of the analytical process are not obvious. Bluhm for instance, stressed that:
 1
     See: http://www.corpusthomisticum.org/, https://salve2.wordpress.com/.


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       [I]t is not always obvious which linguistic phenomena are pertinent for the analyt-
       ical process. Thus, if we take seriously the idea of approaching some philosophical
       problem through an analysis of ordinary language, we first need to clarify which
       expressions are to be considered at all. ([5], 8)
     Bluhm emphasizes an important aspect of corpus-based CA: What are the expressions
of ordinary language that should be considered for analysis? What are the linguistic phe-
nomena to explore? In the same work, Bluhm stressed the fact that philosophers have often
relied on their insights about the functioning of language to define the relevant expressions
to be analyzed according to a research interest. To prevent any error arising from the re-
searcher’s personal knowledge of language, Bluhm proposes the use of linguistic corpora for
philosophical analysis, such as the British National Corpus.2
     This type of observation reveals an important element for this article, which is the rela-
tion between words and concepts. The nature of the relation between concept and language
is the object of various theories of the concept. Among many others [11]; [37] that support
a strong relation between concepts and conceptual expressions, we opt here for Murphy’s
cognitive approach due to its inclusiveness [47]. In his approach, it is impossible to uni-
vocally define the relation between these two entities. The idea that every word matches a
single concept is not acceptable. In fact, several phenomena can prove the contrary, as do
synonymy and polysemy. A word can express many concepts or, conversely, more words
can express just one concept. In addition, there are concepts that do not have any precise
lexical manifestation ([47], 289).
     There are thus different semantic phenomena involved in defining the relation that a
word can have with a concept. One of those is synonymy. In fact, a concept could be expressed
by several lexical forms and all of them can be considered as standard lexical forms. For
example, another standard lexical form of marriage is also the word ”wedding.” In this case,
we have a one-to-many relation, that is, a concept expressed in many ways. In a corpus-based
CA, finding the synonyms of a standard lexical form of the concept is a necessary operation.
For example, if we analyze the concept of human in the writings of Aristotle, we must be
able to retrieve its potential synonyms, such as “rational animal.” Its opposite phenomenon
is polysemy3 , where many concepts are related to one lexical item in a many-to-one relation.
From a semiotic point of view, a standard lexical form is not identifiable with a signifier (the
sequence of characters), but with the two-faced entity of the sign (signifier-signified) that best
conveys the concept. In a computer-assisted CA, it is therefore important to identify only
the occurrences of a word that refer to the concept under study. The classical example is the
signifier ”bank,” which can refer to a financial institution or to the side of a body of water.
Another example might be that of the signifier ”life,” because some uses of this word are not
relevant to a CA of the concept of life, as when it is used to refer to a ”characteristic state
or mode of existence” (i.e. “Lifestyle”). A rather extensive category of phenomena, called
contextual modulation [14], emphasizes the role of context in determining the meaning of
a word. Cruse ([14], 51) suggests that “the meaning of any word form is in some sense
different in every distinct context in which it occurs.” According to Murphy, this general
process of language emphasizes the importance of a “knowledge approach to concepts,”
 2
     See: http://www.natcorp.ox.ac.uk/
 3
     For simplicity, we will just use the concept of polysemy, because it is not easy to clearly distinguish
     between homology and polysemy ([69], 52).


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since the linguistic context of a word and the background knowledge of users are involved
in both the definition of a word’s meaning and in that of a concept. This is a truism for
semiotic studies [70].
     In particular, one typical phenomenon of contextual modulation is more important for
CA and can be understood through the rhetorical phenomenon of ellipsis. In rhetoric, el-
lipsis is a figure of speech that allows the omission of a word or words within a sentence
without its understanding being compromised. [47] exposes an example that recalls this
phenomenon. In sentences like (1) “The accountant pounded the stake” and (2) “The ac-
countant pounded the desk,” the word “pounded” is elaborated on the basis of the linguistic
and pragmatic context, using encyclopedic knowledge. In fact, the understanding of these
sentences involves extra-textual information. The words “mallet” or “hammer” are a recall
cue for the first sentence and the word “fist” is a better recall cue for the second sentence
[68]. This is the rhizomatic model of the encyclopedia, as defined by [20], and it reveals,
more specifically, how a net of different concepts can be involved in the definition of the
meaning of a sentence, even if they do not manifest themselves explicitly in the text. This
also happens, but in a different way, in theoretical texts, such as philosophical texts, wherein
ellipsis must be conceived as a phenomenon characterized by the omission of the standard
lexical form of a concept. Thus, in a CA, if we look at the standard lexical form “noth-
ingness” of the concept of nothingness, we must consider that the concept can also emerge
in text segments in which the lexical form “nothingness” does not appear, even though it
remains underlying.
     In a corpus-based CA, all these considerations cannot be overlooked. Answering ques-
tions like ”What is nothingness in Kierkegaard’s works?” means analyzing all those text seg-
ments that concern the concept of nothingness. For a CA, it is crucial to find the most relevant
text segments for the analysis. Some of these segments can be easily identified if we use the
standard lexical form of the concept, that is, the words that best convey the concept under
study. It is difficult to take issue with the pertinence of this kind of text segment in a CA.
However, other types of equally relevant segments are not so easily retrievable, for instance
the segments where linguistic phenomena like synonymy, polysemy, or contextual mod-
ulation are involved. Some of these phenomena may have simple solutions. For example,
synonymy or polysemy can be solved by using a dictionary or by means of some encyclope-
dic knowledge. Other phenomena, on the other hand, do not have such simple solutions,
such as various cases of contextual modulation.

3.2 Distributional Hypothesis and Semantic Vector Space Models
If the analysis is based on a small set of texts, this kind of operation does not cause major
problems. The identification of relevant segments for CA becomes complex and laborious
when the corpus is large. Owing to the advancement of AI research, there are several tools
that can be used in text analysis. The computational approach employed in this article is
based on a well-defined linguistic theory which derives from structuralism: the distribu-
tional hypothesis. The distributional paradigm, developed by Zellig Harris and grounded on
Leonard Bloomfield’s works, is essentially based on the notion that words that occur in the
same contexts tend to have similar meanings [32]. Firth coined this principle with the fol-
lowing famous sentence: “You shall know a word by the company it keeps” [27]. In other
words, two words tend to be similar if they often stand in the vicinity of the same words.


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    This theory has paved the way for the Semantic Vector Space Model (SVSM) [62], a
mathematical model for representing text documents in a vector space. The hypothesis of
this model is that the meaning of a text may be represented by means of a vector in a hyper-
dimensional space whose coordinates derive from the frequency of words the text contains.
The simplest SVSM is thus based on the frequency of words in texts and it identifies similar
texts by the amount of words they share.

4 Method and Experimentation
Each experiment has methodological specificities that will be presented separately. However,
there are many shared methodological elements which will be outlined below. Overall, the
three experiments present elements that can be used in a more general computer-assisted
text analysis methodology.

4.1 Corpus and CA
Let us then introduce the most general methodological elements. The corpus under study is
the Collected Papers (CP), one of the largest collections of C. S. Peirce’s writings, containing
eight volumes published by the Harvard University Press between 1931 and 1956 [50].
These writings contain approximately 3,000 pages, with 5,163 paragraphs. Being one of
the founding fathers of American pragmatism, Peirce still remains an unavoidable reference
in philosophy and semiotics.
    The concept used for our experimentation is mind, which is one of the most important
of Peirce’s entire work [55]. Studying this concept in Peirce’s works is a complex operation,
since it is one of the most commonly studied subjects in philosophy [60]. However, getting
into the various philosophical theses or debates about the concept of mind (the mind-body
problem, the relation with consciousness, etc.) is not an objective of this article.
    Analyzing the concept of mind means to start from a standard lexical form of the con-
cept, that is, the word “mind.” However, in order to identify the standard lexical form of
the concept, a simple morphological analysis of the word “mind” must be taken into ac-
count to disambiguate it. According to the Oxford Dictionary,4 the word resulting from
the chain of characters making up the lexical unit “mind” can be both a noun and a verb.
Usually, the uses of the verb “mind” in sentences such as “I do not mind the noise during
the day” or “Do you mind if I ask you one more thing?” do not concern the concept of
mind. Furthermore, the noun “mind” has multiple meanings,5 such as “recollection or re-
membrance,”“opinion or sentiment,”“inclination or desire,” etc. Among these, the meaning
“the human faculty to which are ascribed thought, feeling, etc.” is the most related to the
concept of mind. This analysis has been taken into account in the experiments.

4.2 Pre-Processing
Each experiment we conducted required pre-processing of the textual data. In order to pre-
pare the corpus for computer-assisted CA, it is necessary to perform some operations that al-
low us to extract the linguistic information needed for the analysis. These operations are the
 4
     See: https://en.oxforddictionaries.com
 5
     https://www.collinsdictionary.com/dictionary/english/mind.


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                                                                                   Peirce
following: 1) Sentence Boundary Detection, 2) Tokenization, 3) Part of Speech (POS)Tagging,
4) Vectorization.
     Boundary sentence detection is a classic NLP operation, which can be solved using
different systems such as the direct encoding of knowledge, a rule-based learning system,
static maximum-entropy learning algorithms, etc. [71]. We have used a rule-based learning
algorithm6 and, at the end of this process, we found 44,814 sentences. The tokenization
operation consists in identifying each orthographic unit of each sentence. The POS tagging
process was performed on the basis of The Penn Treebank project [42], which is one of the
major references for English. This operation consists in the annotation of each orthographic
unit according to its morphological category, such as names, pronouns, verbs, adjectives,
numbers, etc. This allows the elimination of some categories of words such as determinants,
prepositions, and pronouns, which are not interesting for the analysis. Instead, nouns, verbs,
modals, adjectives, adverbs, proper nouns, and foreign words were retained. At the end of
this process, we identified 9,668 distinct words, which are called types.
     Finally, the last operation was the mathematical modeling of each sentence into a vector
space. In this model, each sentence is encoded by a vector whose coordinates correspond
to the TF-IDF weighting pattern of the words occurring in that sentence. Specifically, this
weighting function calculates the normalized frequencies of the words of each sentence [63].
At the end of the process, a matrix M was built containing 44,814 rows corresponding to
the sentences found in the corpus and 9,668 dimensions, corresponding to the types.

4.3 Other Shared Methodological Elements
To assist the interpretation and evaluation of the results obtained in the three experiments,
we relied on a computer-assisted tool and on a human analysis of the concept of mind in
Peirce’s Collected Papers. The first one is a classic tool of IR, the cosine computation among
vectors, which is generally used to select relevant documents. For each experiment, a query
vector was calculated in order to select some representative sentences. The second was a short
qualitative description of the concept of mind in Peirce, based on the work of some philoso-
phers who have already studied the topic. This was used as a sort of qualitative benchmark
for evaluating the results of each experiment. We provide this description below.

4.4 Mind in Peirce
Studies about the concept of mind in Peirce’s writings are numerous and it is not easy to
give an overview of it. However, a brief description of the concept of mind was used to
evaluate and interpret the segments retrieved in the experiments. In general, we can state
that Peirce adopts a real “semiotic approach to the human mind” ([13], 508). For Peirce,
“because the totality of the mind’s manifestations are signs, we are warranted in identifying
mind with semiosis” (CP 5.313). Thus, the study of the mind also concerns the study of
the “logic of symbols and signs” ([52], 201). Another important aspect is the relationship
between the concept of mind and consciousness. For Peirce, there seems to be no distinction
between the content of consciousness and the manifestation of mind, since both of them
are resolved in the sign resulting from an inference (CP 5.313). However, the essence of
 6
     See the segtok tool for python.


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mind is not conscience, but purpose, or final causation ([66], 554). But what best describes
Peirce’s idea of mind is the concept of law of mind that is “the process by which ideas grow”
([29], 90), a kind of continuous spread of ideas that constitutes the mind. This brings us to
another important element of Peirce’s philosophy, that is, the immaterial dimension of the
mind. For Peirce, the mind is not in the brain, inasmuch as electricity is not in copper wires
([66], 553). The mind is where thought can be expressed, that is, in “paper or other vehicles
for preserving and conveying thoughts” ([66], 553). This highlights another element of his
philosophy of mind: It is thought that constitutes it, thereby dissociating the mind from a
specific material substrate. In fact, thoughts, as mind, are not in the brain: “In my opinion
it is much more true that the thoughts of a living writer are in any printed copy of his
book than they are in his brain” (CP 7.364). This connection between mind and thought is
supported by his sign theory, since “what makes them thought processes is the sign character
of the thoughts” ([66], 554). This makes it possible to express and interpret them. Some
other aspects of his philosophy of mind are not easy to summarize, such as the triadic relation
between habit-sign-mind, leading to a conception of “the human mind [as] an incredibly
complex and hierarchically ordered network of habits” ([13], 501), or the relation between
mind and reasoning and its links with self-consciousness, self-criticism, and self-control ([13],
491).


4.5 Experiment No. 1: Synonymy

The first experiment dealt with the semantic phenomenon of synonymy in CA. The aim was
to explore how a cosine-based IR algorithm can enable us to retrieve synonyms of “mind”
in Pierce’s Collected Papers and to retrieve relevant text segments for a CA of mind. For this
experiment, a simple method consisting of two steps was used: first, building a similarity
matrix, and second, retrieving relevant text segments with a query vector.


                                  Table 1. Similarity of ”mind”

                                        Word: Cosine Value
                                       thought (noun): 0.8237
                                         think (verb): 0.8233
                                        really (adverb): 0.8178
                                           do (verb): 0.8088
                                        nature (noun): 0.8013
                                        thing (noun): 0.7990
                                         seem (verb): 0.7973
                                         come (verb): 0.7968
                                         make (verb): 0.7960
                                       reason (noun): 0.7935




    The similarity matrix P was computed from the M matrix, and it represents all the
relations of similarity of each word with all of the others. This was obtained by multiplying
the M matrix with its own transpose and by normalizing the result. So, attributes of each
vector of P are the scalar products of the distribution of a word for the distribution of every
other word. We then got the vector c representing the word “mind” according to its relations


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                                                                                Peirce
of similarity. Subsequently, we could analyze the relation between the vector c and all the
other vectors representing each word. The results (Table 1) show that the first nouns closest
to mind are “thought”, “nature”, “thing”, “reason”. It is also interesting to note that the most
similar verb to “mind” is “think”. In light of Paragraph 4.4, it is not surprising to note that
thought is linked to the concept of mind.




                               Table 2. Closest Sentences to Vector q

                  Paragraph Code                                             Sentence
                                                   ”Idea” is here to be understood in a sort of Platonic sense,
                                                  very familiar in everyday talk; I mean in that sense in which
                                                    we say that one man catches another man’s idea, in which
                                                   we say that when a man recalls what he was thinking of at
                    CP 2.228                       some previous time, he recalls the same idea, and in which
                                                  when a man continues to think anything, say for a tenth of a
                                                  second, in so far as the thought continues to agree with itself
                                                  during that time, that is to have a like content, it is the same
                                                    idea, and is not at each instant of the interval a new idea.
                                                  And an idea can not be thought, except when it is present in
                    CP 7.349
                                                                          the mind.
                                                  In its broader sense, it is the science of the necessary laws of
                                                     thought, or, still better (thought always taking place by
                                                   means of signs), it is general semeiotic, treating not merely
                                                    of truth, but also of the general conditions of signs being
                                                  signs (which Duns Scotus called grammatica speculativa 1),
                                                   also of the laws of the evolution of thought, which since it
                    CP 1.444                       coincides with the study of the necessary conditions of the
                                                   transmission of meaning by signs from mind to mind, and
                                                    from one state of mind to another, ought, for the sake of
                                                    taking advantage of an old association of terms, be called
                                                      rhetorica speculativa, but which I content myself with
                                                  inaccurately calling objective logic, because that conveys the
                                                              correct idea that it is like Hegel’s logic.
                                                   But observing that ”the external” means simply that which
                                                       is independent of what phenomenon is immediately
                                                    present, that is of how we may think or feel; just as ”the
                                                  real” means that which is independent of how we may think
                                                     or feel about it; it must be granted that there are many
                     CP 8.13
                                                   objects of true science which are external, because there are
                                                   many objects of thought which, if they are independent of
                                                   that thinking whereby they are thought (that is, if they are
                                                  real), are indisputably independent of all other thoughts and
                                                                              feelings.
                                                        And this causation is necessarily of the nature of a
                                                       reproduction; because if a thought of a certain kind
                                                  continues for a certain length of time as it must do to come
                    CP 7.353
                                                    into consciousness the immediate effect produced by this
                                                  causality must also be present during the whole time, so that
                                                                   it is a part of that thought.
                                                   The idea of the present instant, which, whether it exists or
                                                    not, is naturally thought as a point of time in which no
                    CP 8.329
                                                  thought can take place or any detail be separated, is an idea
                                                                          of Firstness.



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    Using the noun ”thought” as a synonym, a query vector q was computed with which
it was possible to select some typical sentences representing the relationship of similarity
between these two words. The vector q is the result of the sum of c and g, where g is the vector
representing the similarities to the word “thought”. Using a cosine computation between
query vector q and all vectors in the original matrix M, we selected some typical sentences
that represent the relationship between “mind” and “thought” within the corpus.
    As one can see, these sentences show that for Peirce there exists some similarity of mean-
ing between the nouns “mind” and “thought.” This kind of similarity is very important in
CA, because it enables us to find some particular properties of the concept under study. For
example, in Peirce’s Collected Papers, these two words share the same relation with “ideas”,
both as a word and as a concept. As mentioned in Paragraph 4.4, this is a central element
of the concept of law of mind.

4.6 Experiment No. 2: Polysemy
The second experiment dealt with the problem of polysemy in CA. As mentioned above, the
standard lexical form of the concept was identified as the noun “mind”. However, the latter
does not have an exclusive relation with the concept of mind and can be used in different
contexts with reference to different meanings. Polysemy is one of those linguistic phenom-
ena that interfere with the relation between language and concept, also affecting the process
of selecting the text segments to be analyzed during a CA. For this experiment, an unsuper-
vised approach was used. To disambiguate, some tools common in ML were employed, such
as clustering and silhouette analysis. In a text mining framework, clustering algorithms group
documents that share similar features, usually word co-occurrence patterns, in order to dis-
cover the semantic structures that characterize the documents. In our experiment, we used
the K-means algorithm ([35], 50), a widely employed algorithm for word-sense disambigua-
tion tasks [49]; [54]. The main parameter that needed to be tuned in the K-means algorithm
was the k, which determines the number of clusters. The silhouette index was employed to
set this parameter. This index is used as an internal validation measure for clustering [58]
because it evaluates the quality of a partition by means of two criteria: the compactness and
the separation of clusters. Whenever K-means performs a partition, the silhouette index
analyzes the quality of the clusters created. The greater is the similarity within the clusters
(compactness) and the greater is the distance between the various clusters (separation), the
better is the partition [40].
     In our experiment, the silhouette analysis evaluated the first 50 possible partitions of
all those text segments containing the noun “mind.” The K-means algorithm was applied
to all of the text segments where the noun “mind” appears. By finding groups in these text
segments, the algorithm identifies different semantic structures reflecting different senses or
usages of the chosen keyword. Thus, the algorithm could disambiguate the noun “mind” in
Peirce’s Collected Papers. As plotted in Figure No. 1, according to the silhouette index, one
of the best partitions is a K-means clustering into 16 groups.
     Some groups of the 16-cluster partition are shown in Table 3 and one can see how the
concept of mind is related with theory of signs, humanity, ontology, matter, ideas, thought,
time, law of mind, conscience, feelings, etc. In this experiment, different meanings of the
signifier “mind” have been searched. However, cluster analysis suggests that in the corpus
analyzed there is no recurrent semantic structure deriving from the noun “mind” that does


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not concern the concept of mind. In fact, these results disambiguate the uses of the noun
“mind,” thus identifying some dimensions of the concept of mind which match the con-
ceptual map described in Paragraph 4.4. So, every sentence in which the noun “mind” is
present is potentially relevant for a CA regarding the concept of mind in Peirce. For some
clusters, we provide a sample of the typical sentence that shows some proprieties of the con-
cept of mind in Peirce’s work. For each cluster, the most typical sentences have been selected
by cosine calculation between the qi vector, used as a query vector, and each sentence di in
the corpus, where qi is the centroid of each cluster, which is computed using the K-means
algorithm. The limits of this method will be discussed in the Conclusions section.

                         Table 3: Clustering Results of ”mind” Uses

Cluster Code       Paragraph Code: Sentence
Clust 0            8.262: Moreover, the human mind and the human heart have a filiation
                   to God.
                   7.380: It is the human mind that is infinite.
                   6.95: The first step of Kant’s thought – the first moment of it, if you like
                   that phraseology – is to recognize that all our knowledge is, and forever
                   must be, relative to human experience and to the nature of the human
                   mind.
Clust 1            CP 8.29: It is said that Matter exists without the mind.
                   CP 4.447: It exists only as an image in the mind.
                   CP 7.342: So that his knowledge of the thing which exists all the time,
                   exists only by virtue of the fact that when a certain occasion arises a certain
                   idea will come into his mind.
Clust 3            CP 6.101: We know very well that mind, in some sense, acts on matter,
                   and matter on mind: the question is how.
                   CP 4.611: Now whether this particular way of solving the paradox hap-
                   pens to be the actual way, or not, it suffices to show us that from the
                   supposed fact that mind acts immediately only on mind, and matter im-
                   mediately only on matter, it by no means follows that mind cannot act
                   on matter, and matter on mind, without any tertium quid.
                   CP 7.369: Nobody would doubt that that was the true account of the
                   matter, were it not that it is contrary to the law of dynamics that mind
                   should act on matter and contrary to the law of purpose that matter
                   should act on mind.
Clust 6            CP 6.18: A feeling is a state of mind having its own living quality, inde-
                   pendent of any other state of mind.
                   CP 5.288: Consider a state of mind which is a conception.
                   CP 6.70: Every state of mind, acting under an overruling association,
                   produces another state of mind.




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Clust 7          CP 7.515: Now the generalizing tendency is the great law of mind, the
                 law of association, the law of habit taking.
                 CP 6.277: But it differs essentially from materialism, in that, instead of
                 supposing mind to be governed by blind mechanical law, it supposes the
                 one original law to be the recognized law of mind, the law of association,
                 of which the laws of matter are regarded as mere special results.
                 CP 6.612: In my essay ”The Law of Mind” I have so described that law.
Clust 8          CP 7.349: Two ideas exist at different times; consequently what is present
                 to the mind in one is present only at that time, and is absent at the time
                 when the other idea is present.
                 CP 7.350: Now so long as we suppose that what is present to the mind at
                 one time is absolutely distinct from what is present to the mind at another
                 time, our ideas are absolutely individual, and without any similarity.
                 CP 7.348: There is a process which can only take place in a space of time;
                 but an idea is not present to the mind during a space of time – at least not
                 during a space of time in which this idea is replaced by another; for when
                 the moment of its being present is passed, it is no longer in the mind at
                 all.
Clust 9          CP 1.310: The first is that of whatever is in the mind in any mode of
                 consciousness there is necessarily an immediate consciousness and conse-
                 quently a feeling.
                 CP 7.365: What the psychologists study is mind, not consciousness ex-
                 clusively.
                 CP 7.366: Consciousness, per se, is nothing else: and consciousness, he
                 maintains, is Mind.
Clust 11         CP 7.349: An idea can contain nothing but what is present to the mind
                 in that idea.
                 CP 7.354: This does not mean that I have always had the idea of prussic
                 acid in my mind, but only that on the proper occasion, on thinking of
                 drinking it, for example, the idea of poison and all the other ideas that
                 that idea would bring up, would arise in my mind.
                 CP 7.392: The clustering of ideas into classes is the simplest form which
                 the association of ideas by the occult nature of ideas, or of the mind, can
                 take.




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Clust 12            CP 5.288: Now the logical comprehension of a thought is usually said to
                    consist of the thoughts contained in it; but thoughts are events, acts of
                    the mind.
                    CP 7.339: That is external to the mind, which is what it is, whatever our
                    thoughts may be on any subject; just as that is real which is what it is,
                    whatever our thoughts may be concerning that particular thing.
                    CP 8.40: But ordinary people say that not merely the real but all that can
                    possibly enter into the mind of man must be within the thought of God
                    in some sense; so that it must be some particular kind of divine thought
                    which constitutes reality; and that particular kind of thought must be
                    distinguished by a volitional element.
Clust 14            CP 3.361: Supposing, then, the relation of the sign to its object does not
                    lie in a mental association, there must be a direct dual relation of the sign
                    to its object independent of the mind using the sign.
                    CP 4.536: I have already noted that a Sign has an Object and an Inter-
                    pretant, the latter being that which the Sign produces in the Quasi-mind
                    that is the Interpreter by determining the latter to a feeling, to an exertion,
                    or to a Sign, which determination is the Interpretant.
                    CP 8.179: The Sign creates something in the Mind of the Interpreter,
                    which something, in that it has been so created by the sign, has been, in a
                    mediate and relative way, also created by the Object of the Sign, although
                    the Object is essentially other than the Sign.


    As one can see, each cluster of sentences illustrates different semantic structures related
with the lexical item “mind.” They can be interpreted as “thematic” nuances of the meaning
of the noun “mind” and therefore as indicating some properties of the concept of mind. For
example, cluster No. 3 summarizes the immaterial dimension of mind, cluster No. 7 focuses
on the concept of law of mind, cluster No. 11 emphasizes the role of the spread of ideas
in a theory of mind, cluster No. 12 highlights the relationship between thought and mind,
cluster No. 14 stresses the role of mind in a theory of signs, etc.

4.7 Experiment No. 3: Ellipsis
The last experiment aimed to select relevant elliptical segments for CA. Ellipsis can be seen
as a particular contextual modulation phenomenon involving conceptual expressions. It de-
scribes the semantic process of those text segments where a concept is evoked but without
any standard lexical anchorage. In other words, this experiment retrieved text segments
where the concept of mind is evoked but without its standard lexical form.
    In particular, we formalized this task as a positive and unlabeled data (PU) classification
problem [6]. This kind of method aims to expand the set of positive data from unlabeled
data. The problem of identifying elliptical segments is similar to a PU classification problem
because it is possible to interpret the group of text segments containing the noun “mind”
as the P dataset (positive dataset), which one wants to expand, and to mark as unlabeled
dataset U the rest of the corpus. In other words, we wanted to find the set of elliptical


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segments E, that is, those text segments that, in a PU classification, would be the expanded
positive data found among unlabeled data U.
     As proposed by [48], we used a two-step approach: 1) First, we identified a set of negative
data, forming the negative dataset N. For this task, the DBSCAN clustering algorithm was
used for its capacity to detect outlier data [23], [24], which are observation points that
are distant from a closer set of observations; 2) Then, we identified E as a subset of U
using the co-training algorithm, designed by [6] specifically for the needs related to the PU
classification problem. In a nutshell, we first identified the most dissimilar or distant cases
from P, which were marked as negative data N. Then, we trained an algorithm in order to
classify the U dataset as positive P or negative N.
     In the first step, the U dataset was divided into 22 sets, to which we added the P set,
forming the Di set. For each Di set, the DBSCAN parameters (Eps and MinPts) were tuned
in order to obtain the optimized classifier Si , which aims to find a single large cluster with a
number of outliers and which does not exceed 20% of the Di set size. This rate is an empirical
parameter of the method. At the end of the process, we got 8,731 segments forming the N
dataset, 1,463 forming the P dataset (18 segments that contain the word “mind” have been
marked as outliers), and 34,592 forming the U dataset.
     In the second step, we first separated the unlabeled data U into two different datasets,
to which we added N, forming Z1 and Z2 . Then, we applied the co-training algorithm as
defined by Luo et al. [41]. This algorithm uses two SVM classifiers, which are first trained
on a dataset composed by P as positive data and Zi as negative data. After this first train-
ing phase, these classifiers bootstrap one another by exploring the opposite dataset and by
iteratively feeding a second phase of dynamic learning. This process therefore allowed us
to explore the U dataset through two classifiers which feed one another by means of two
different points of view on data, that is, Z1 and Z2 . At the end of the process, we got the
E dataset, composed of 2,496 text segments. If in a PU classification, E will be the set of
segments that extend P, which is generally used for subsequent supervised learning tasks.
In our experiment, this set was instead studied as a set containing elliptical segments.
     A query vector q was constructed computing the arithmetic mean of the E dataset. Using
q, we retrieved the following typical elliptical segments:

                                  Table 4: Elliptical segments

Paragraph Code      Sentence
CP 6.373            The idea would, therefore, be found in a pure state only in an immediate
                    consciousness which should make no distinction of any kind, whether
                    between subject and object, or of the parts of the object.
CP 7.353            And this causation is necessarily of the nature of a reproduction; because
                    if a thought of a certain kind continues for a certain length of time as it
                    must do to come into consciousness the immediate effect produced by
                    this causality must also be present during the whole time, so that it is a
                    part of that thought.
CP 1.220            Do I mean that the idea calls new matter into existence?
CP 7.345            For if there be an idea of such a reality, it is the object of that idea of
                    which we are speaking, and which is not independent of thought.



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CP 8.16             To make a distinction between the true conception of a thing and the
                    thing itself is, he will say, only to regard one and the same thing from two
                    different points of view; for the immediate object of thought in a true
                    judgment is the reality.
CP 6.214            It has, therefore, to do something like supposing a state of things in which
                    that universe did not exist, and consider how it could have arisen.
CP 4.55             The idea which is the matter of the belief is suggested by the idea in those
                    judgments according to some habit of association, and the peculiar char-
                    acter of believing the idea really is so, is derived from the same element
                    in the judgments.
CP 7.137            The formal laws do not depend on any particular state of things, and
                    hence we say we have not derived them from experience; that is to say,
                    any other experience would have furnished the premisses for them as well
                    as that which we have experienced; while to discover the material laws we
                    require to have known just such facts as we did.


    The results here have been generated exploring the similarity relations between sentences
that contain the word “mind” and sentences that do not contain it. As one can see, this group
of sentences shows that Peirce expresses properties about mind in different ways. Most of
them are to be retained in a conceptual analysis that aims to be as exhaustive as possible.
For example, some elliptical sentences focus on consciousness (CP 6.373, CP 7.53), some
on idea (CP 1.220, CP 7.345), some on thought (CP 7.345, CP 8.16), some on habit of
association of ideas (CP 4.55), etc.

5 Conclusions
Three experiments have been presented in this article, each of them dealing with a particu-
lar semantic phenomenon involved in the process of determining a conceptual expression.
Each experiment has detected relevant text segments for a CA of mind in Peirce’s Collected
Papers, showing that, to be exhaustive, a corpus-based computer-assisted CA has to deal
with synonymy, polysemy, and elliptical segments. Results are encouraging as regards the
overcoming of our method’s limitations.
     In the first experiment, the main problem lied in the difficulty of distinguishing different
aspects of the relation of similarity between words. As mentioned above, the distributional
hypothesis states that words with similar meanings tend to appear in similar contexts. Many
works on synonymy are based on this hypothesis [21] and one of the simplest methods is
to analyze the similarities between words through their distribution in the corpus [33];
[40]. This is the approach we followed. However, some have criticized this model because
it is incapable of distinguishing the various semantic properties of semantic similarity [9];
[26]. Alternative models have been proposed, which, for example, differentiate between
synonymy and antinomy [64]. Overcoming these limitations could improve our algorithm
for detecting relevant text segments in a context of synonymy.
     In the second experiment, the main problem concerned the difficulty of drawing a dis-
tinction between a word’s different meanings and its different usages. With regard to NLP


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and ML, the phenomenon of polysemy is studied in word-sense disambiguation research,
which is carried out along three main approaches. A first approach is the Knowledge-Based
approach, where disambiguation is performed by an algorithm that compares the occur-
rence of a word with the definitions of a dictionary or ontology, such as the Lesk algorithm.
Another one is the Supervised Corpus-Based approach, where sense-annotated corpora, such
as Senseval or SemCor, are used for training algorithms to disambiguate word senses. A
third one is the Unsupervised Corpus-Based approach, where the disambiguation is carried
out by a clustering algorithm which gathers different uses of a word by only analyzing se-
mantic relationships found in the corpus analyzed. In our second experiment, we followed
the unsupervised approach, which does not, in contrast to the other approaches, use ex-
ternal knowledge for disambiguation purposes. Dictionaries and ontologies are employed
to recognize different meanings of a word, thus also to identify its uses. To our knowledge,
however, there is no corpus or external ontology that is capable of assisting a disambiguation
process for a philosophical text.
     In the third experiment, the method based itself on the postulate that elliptical segments
are semantically similar to those segments containing the noun “mind” (positive dataset P).
This does not permit to retrieve other kinds of elliptical segments. However, we overcame the
limitations of our previous works [53]; [12] where the two unsupervised learning methods
depended on some more specific subsets of the positive dataset P. Conversely, the semi-
supervised approach used in this article allows us to identify elliptical segments in a more
global way.
     Lastly, this article does not distinguish the logical relations between the various segments
found. For example, our method does not allow us to detect definitions, inferences, impli-
cations, paraphrases, metalinguistic reformulations, etc. All of these topics are the subject
of important theoretical debates pertaining to the analysis of concepts in text corpora. This
type of task could be assisted with some tools developed in argument mining research [57].
     The main contribution of this article is the exploration of the computational approach
to CA, developing methods and showing its great potentiality. Another contribution is the
transfer of knowledge from AI to the humanities, because these processing chains provide
assistance in analytical practices which are widespread in the humanities. Moreover, the
article also suggests a new area for text mining research, which is computer-assisted CA,
and emphasizes theoretical items that are often neglected, such as the difference between
concept and lexeme.


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    Last consultation URLs: 03/03/2018




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