Text analysis: linguistics, Semantics, Pragmatics in the Cognitive Approach
The cognitive approach to linguistic information analyzed by the human is considered. The processes of information processing are studied at various linguistic levels: morphological, lexical, syntactic and semantic levels for separate sentences, and finally, semantic and pragmatic levels for the tex...
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nasplib_isofts_kiev_ua-123456789-1125692025-02-23T18:19:40Z Text analysis: linguistics, Semantics, Pragmatics in the Cognitive Approach Аналіз тексту: лінгвістика, семантика, прагматика в когнітивному підході Анализ текста: лингвистика, семантика, прагматика в когнитивном подходе Kharlamov, A.A. Yermolenko, T.V. Моделирование восприятия внешнего мира The cognitive approach to linguistic information analyzed by the human is considered. The processes of information processing are studied at various linguistic levels: morphological, lexical, syntactic and semantic levels for separate sentences, and finally, semantic and pragmatic levels for the text as a whole. As an example of the following processing, representation of the pragmatic level is identified as a chain of extended predicate structures of a particular text processing, pragmatic processing, chain of extended predicate structures. Розглянуто когнітивний підхід до аналізу лінгвістичної інформації та процеси обробки інформації різних лінгвістичних рівнів: морфологічного, лексичного та ін. Як приклад подано ланцюжок розширених предикатних структур конкретного тексту. Рассмотрены когнитивный подход к анализу лингвистической информации и процессы обработки информации разных лингвистических уровней: морфологического, синтаксического и др. В качестве примера приведена цепочка расширенных предикатных структур конкретного текста. The works was performed within the research “Study of the mechanism of associative links in human verbal and cogitative activity using the method of neural network modeling in the analysis of textual information” (with financial support from the Russian Foundation for Basic Research, grant 14-06-00363). 2015 Article Text analysis: linguistics, Semantics, Pragmatics in the Cognitive Approach / A.A. Kharlamov, T.V. Yermolenko // Управляющие системы и машины. — 2015. — № 6. — С. 29–33. — Бібліогр.: 7 назв. — англ. 0130-5395 https://nasplib.isofts.kiev.ua/handle/123456789/112569 004. 934 en Управляющие системы и машины application/pdf Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України |
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Моделирование восприятия внешнего мира Моделирование восприятия внешнего мира |
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Моделирование восприятия внешнего мира Моделирование восприятия внешнего мира Kharlamov, A.A. Yermolenko, T.V. Text analysis: linguistics, Semantics, Pragmatics in the Cognitive Approach Управляющие системы и машины |
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The cognitive approach to linguistic information analyzed by the human is considered. The processes of information processing are studied at various linguistic levels: morphological, lexical, syntactic and semantic levels for separate sentences, and finally, semantic and pragmatic levels for the text as a whole. As an example of the following processing, representation of the pragmatic level is identified as a chain of extended predicate structures of a particular text processing, pragmatic processing, chain of extended predicate structures. |
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Article |
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Kharlamov, A.A. Yermolenko, T.V. |
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Kharlamov, A.A. Yermolenko, T.V. |
| author_sort |
Kharlamov, A.A. |
| title |
Text analysis: linguistics, Semantics, Pragmatics in the Cognitive Approach |
| title_short |
Text analysis: linguistics, Semantics, Pragmatics in the Cognitive Approach |
| title_full |
Text analysis: linguistics, Semantics, Pragmatics in the Cognitive Approach |
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Text analysis: linguistics, Semantics, Pragmatics in the Cognitive Approach |
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Text analysis: linguistics, Semantics, Pragmatics in the Cognitive Approach |
| title_sort |
text analysis: linguistics, semantics, pragmatics in the cognitive approach |
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Міжнародний науково-навчальний центр інформаційних технологій і систем НАН та МОН України |
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2015 |
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Моделирование восприятия внешнего мира |
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Text analysis: linguistics, Semantics, Pragmatics in the Cognitive Approach / A.A. Kharlamov, T.V. Yermolenko // Управляющие системы и машины. — 2015. — № 6. — С. 29–33. — Бібліогр.: 7 назв. — англ. |
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Управляющие системы и машины |
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УСиМ, 2015, № 6 29
УДК 004.934
A.A. Kharlamov, T.V. Yermolenko
Text analysis: linguistics, Semantics, Pragmatics in the Cognitive Approach
Рассмотрены когнитивный подход к анализу лингвистической информации и процессы обработки информации разных лин-
гвистических уровней: морфологического, синтаксического и др. В качестве примера приведена цепочка расширенных преди-
катных структур конкретного текста.
Ключевые слова: автоматическая обработка текстов, когнитивный подход, морфологическая обработка, синтаксическая об-
работка, семантическая обработка, прагматическая обработка, цепочка расширенных предикатных структур.
The cognitive approach to linguistic information analyzed by the human is considered. The processes of information processing are studied at
various linguistic levels: morphological, lexical, syntactic and semantic levels for separate sentences, and finally, semantic and pragmatic levels
for the text as a whole. As an example of the following processing, representation of the pragmatic level is identified as a chain of extended
predicate structures of a particular text.
Keywords: automatic text processing, cognitive approach, morphological processing, lexical processing, syntactic processing, semantic
processing, pragmatic processing, chain of extended predicate structures.
Розглянуто когнітивний підхід до аналізу лінгвістичної інформації та процеси обробки інформації різних лінгвістичних рівнів:
морфологічного, лексичного та ін. Як приклад подано ланцюжок розширених предикатних структур конкретного тексту.
Ключові слова: автоматичне оброблення текстів, когнітивний підхід, морфологічне оброблення, синтаксичне оброблення,
семантичне оброблення, прагматичне оброблення, ланцюжок розширених предикатних структур.
Introduction. Currently, two basic approaches
prevail in the automatic semantic analysis of texts:
linguistic and statistical ones. The first one pro-
vides a very detailed analysis of the meaning of
the text sentences [1], and the second one makes it
possible to create a semantic representation of the
whole text [2]. They do not get along with each
other; there are virtually no papers describing their
joint application, which is explained by a signifi-
cant difference of their implementation mecha-
nisms. In the first case it is pure linguistics, and in
the second case it is pure mathematics. However,
their combined application could make it possible
to obtain semantic representations of the whole
text using fast algorithms of the statistical analysis
with accuracy typical for the linguistic analysis.
There is a possibility of reconciliation of the lin-
guistic and statistical approaches to the text analysis.
For this we use the notions of information proces-
sing (including textual information) by human. To
put it in a nutshell, the specific information proces-
sing in the human brain is reduced to its accumula-
tion in the columns of the cerebral cortex of the
brain [3], and its ranking in the hippocampus. The
columns of the cortex are formed and stored diction-
aries of event images (quasi-words from quasi-texts,
including natural language texts) of various frequen-
cies for various modalities. In the hippocampus
ranking of these representations occurs, which cha-
racterizes the significance of these representations in
individual situations (quasi-texts).
Human linguistic information processing con-
siders the processing of text information at various
levels (linguistic information – morphology, lexis,
syntax, and supralinguistic information – seman-
tics and pragmatics) in terms of the structural
analysis, with natural transitions from one proc-
essing level to another one.
Associative transformation. Cortical neurons
collectively simulate multidimensional space and
provide mapping of input sensor sequences in tra-
jectories of this space [2].
Suppose we have an n-dimensional signal space
Rn and a unit hypercube Gn Rn in it.
Using G(n, N) let us denote a set of sequences
of length N, the elements of which – points of the
Rn space – are vertices of the unit hypercube Gn.
Here G(1, N) Rn – the set of sequences of length
N (N is an arbitrary natural number), the elements
of which are binary numbers.
Definition 1. The trajectory is a sequence
Â: Â G(n, N) n, N > 1. (1)
Indeed, if we consistently connect the points,
which are the elements of the sequence Â, we ob-
tain a trajectory in the Rn space.
Definition 2. N-termed fragment is a frag-
ment of length n of the sequence A G (1, N).
30 УСиМ, 2015, № 6
Let us introduce the transformation Fn of the
one-dimensional sequence in the trajectory  of
the multidimensional Rn space (2):
)1,(),1(: nNnGNGFn , ˆ( )nF A A , (2)
where
1( ( ) : ( ) 0,1 )N
tA a t a t ,
1
1
ˆ ˆ ˆ( ( ) : ( ) ( ( 1), 1, ))N n
tA a t a t a t i i n
,
that is, Â is a sequence of vectors ˆna in the mul-
tidimensional space.
In the general case, the input sequence A may
contain similar n-termed fragments which results
in self-intersection points of the trajectory.
The inverse transformation (2) is computed ac-
cording to (3):
1 : ( , ) (1, 1 )nF G n N G N n , 1 ˆ( )nF A A , (3)
Where 1
ˆ ˆ ˆ( ( ) : ( ) ( ( 1), 1, ))N
tA a t a t a t i i n , and
1
1
1 1
ˆ ( ), 1
( ) : ( )
ˆ ( ),
N n
i N i
a i i N
A a i a i
a N N i N n
.
Formation of level-by-level dictionaries. The
memory mechanism that is sensitive to the num-
ber of passages of a given point in a given direc-
tion is a tool for analyzing the input sequence
from the perspective of its repeating parts. As it is
shown above, similar sequence fragments are
mapped by the transformation Fn into the same
part of the trajectory  in the multidimensional Rn
space.
The dictionary forming is based on the analysis
of multiple sequences {Ak}, in each of which, by
superposition HhRMFn (mapping Fn the sequences
of {Ak} class, into the n-dimensional space,
memorizing M the number of passages by the tra-
jectory of a particular point in the neuron memory,
reading R the contents of the memory of all neu-
rons, and application of the threshold transforma-
tion Hh to them) subsequences {Bj} Ak are iden-
tified that occur in it at least h times (where h is
the threshold value of the threshold transformation
Hh). Thus, the transformation HhRMFn when inter-
acting with the input set {Ak} generates a diction-
ary ˆ{ }jB describing the trajectories correspond-
ing to the subsequences Bj of the input set in the
Rn space of a given dimensionality:
ˆ
j h n kB H RMF A . (4)
Depending on the threshold h value of the
threshold transformation H, words of the diction-
ary ˆ
jB can be trees or graphs containing cycles.
Formation of syntactic sequences. The pre-
formed dictionary can be used to detect old infor-
mation ( ˆ{ }jB dictionary words) in the new in-
formation stream (in the input sequence à differ-
ing from the sequences of the set {Ak} forming the
dictionary). For this, the absorption of the  tra-
jectory fragments of the input à sequence is re-
quired that corresponds to the ˆ
jB dictionary
words, as well as passing of new information
(their links) regarding the dictionary.
To solve the problem of detection, the transfor-
mation 1
nF is modified to add detecting properties
to it. Using the transformation 1
,n CF allows the
formation of the so-called syntactic sequence or se-
quence of abbreviations C characterizing the links of
the ˆ{ }jB dictionary words in sequences of the set
{Ak}. Let us denote by {Bj} a set of subsequences
corresponding to all chains of the ˆ
jB dictionary (4)
words. Then:
1
,
ˆ ˆ( ,{ })n CF A B C (5)
( ) : ( )
ˆ ˆ ˆ0, , : ( ( ), ..., ( )) { },
, -1,.., ,
( ), or else
C c t c t
если l k a l a l k B
t Nl t l k
a t
1
,
1
,
ˆ( ),
ˆ( ), .
n C n h
n C n
C F F A H RM A
F F A B
(6)
Thus, the mapping 1
n,CF allows elimination of
some words contained in the dictionary ˆ{ }jB from
the input sequence Ã. As a result, a structured ap-
proach to information processing is implemented:
first elements of the structure are identified, and then
links between them. The syntactic sequence C con-
taining only new information in regard to the dic-
УСиМ, 2015, № 6 31
tionary of this level becomes the input sequence for
the next level of processing. At the next level, simi-
larly to the level described above, the set of syntactic
sequences {C} forms the dictionary D̂ and the
set of syntactic sequences of the next level {E}.
Thus, we have a standard two-level element of a
multi-level hierarchical structure. Such processing
with identification of level-by-level dictionaries oc-
curs at all levels.
Text analysis. In the text analysis at the stage
of the morphological processing a dictionary of
the first level is formed, {Bj}1 – a dictionary of
inflexions. Then the dictionary of the second level
is formed, {Bk}2 – a dictionary of stems. Next, the
following dictionaries are formed: the dictionary
of the third level {Bl}3 – a dictionary of inflec-
tional structures of syntactic groups, and the dic-
tionary of the fourth level {Bm}4 – a dictionary of
pairwise occurrence of stems in the text. This co-
occurrence is characterized by associations be-
tween these words, in other words, it means the
semantic uncorrectness of the sentence (“Color-
less green ideas sleep furiously”).
Let us introduce the concept of the asterisk [4].
We will call a syntactic structure of the type:
d = <ci <cj >> = j <ci cj>, (7)
where ci is the dominant word, <cj> is a set of
subordinate words, sematic features of the word ci,
an “asterisk”.
Statistical analysis of the text
Formation of the associative network of the
whole text. The statistical analysis of the text is re-
duced to identification of the frequency pi of words
in the text, and to identification of the pairwise oc-
currence pij of words in semantic fragments of the
text. The pairwise occurrence characterizes the se-
mantic co-occurrence of words in the language [5].
In simple cases of the text statistical analysis, to
make the analysis more stable, and the results more
interpretable, word forms of words are reduced to
their radicals. At this a dictionary of stems {Bk}2,
and a dictionary of stems pairwise co-occurrence
{Bm}4 are formed. Thus identified stems serve fur-
ther as the elements for constructing an associative
(homogeneous semantic) network.
The associative (homogeneous semantic) net-
work N is a set of non-symmetrical pairs of no-
tions (stems) <ci cj>, where ci and cj are notions
(stems) connected with an associativity relation
(co-occurrence in a text fragment, for example, in
a sentence) <ci cj> = Bi {Bi}4:
N = i<ci cj>. (8)
In this case, pairs of stems are linked through
the same stems: <c1 c2>*<c2 c3>, where (*) means
adjunction from the right. The result is a chain
<c1 c2 c3>, to which other pairs are further joined.
At this branching and occurrences are possible,
thus, actually a network is built.
If all pairs of words with the same first word
are preliminarily grouped in an asterisk d =
= <ci <cj>> = j <ci cj> (where ci is the dominant
word, <cj> is a set of its semantic features), it can
be said that the network can be built by groups of
all asterisks:
N = i<ci <cj>>. (9)
Notions reranking. Elements of the semantic
(associative) network N = i<ci <cj>> and their
links have numerical characteristics that reflect
their relative weight in a given subject area – their
semantic weight. To estimate the scale of seman-
tic notions more accurately, weights of all related
notions are used, i.e. weights of a “semantic con-
stellation”. As a result of the iterative reranking
procedure, in each iteration notions associated
with notions that have large weights, increase
their own weight. Others lose it evenly:
( 1) ( ( ) ) ( )i i ij
i
i j
w t w t w E
. (10)
here wi(0) = pi, wij = pij/pj and ( ) 1/ (1 )kEE e
is a function normalizing energies of all vertices
of the network E to the average value, where pi is
the frequency of the i-th word in the text, pij is co-
occurrence frequency of the i-th and j-th word in
fragments of the text (sentences). The resulting
numerical characteristic of the words – their se-
mantic weight – characterizes the degree of their
significance (importance) in the text.
Full linguistic analysis of the text sentences
In full linguistic processing at the graphemic
level of the analysis the text is segmented into words
32 УСиМ, 2015, № 6
and sentences, at the morphological level all the
morphological information about words {Bj}1={mj}
is identified, and at the syntactic level – the informa-
tion about the links of words in groups and between
groups {Bk}1={rk}, where rk is a predicative link of
the subject with the main object, and а rk | k > 1 are
all other types of links. The structures of the syntac-
tic level fall within the dictionary of templates for
minimal structural patterns of the sentence and the
dictionary of the verb valencies [6].
In the case of full linguistic processing for each
simple sentence, an extended-predicate structure can
be built, which after some transformations also re-
duces to an asterisk d = j <ci rk cj>, where ci is a
subject, r1 – predicate, and cj – it actants. In the as-
terisk built from the extended predicate structure, the
pair <dominant word, subordinate word> is comple-
mented with a link between them marked with one
of the k types of the relation “predicate-actant” [7].
Integration of the approaches. Semantic and
pragmatic analysis of the whole text
Semantic analysis of the whole text. If an ex-
tended predicate structure of the sentence is iden-
tified using the full linguistic analysis of the sen-
tence, then brought to the form of an asterisk, and
then a semantic network is built using these aster-
isks, and its vertices are reranked, then a network
is obtained in which associative links are replaced
by the corresponding types of links. In this case,
unlike an asterisk with simple associative links, in
an asterisk built from the extended predicate struc-
ture, instead of pairs of notions triples <ci ri cj>
are used, where between a pair of notions there is
a link marked with one of the relation types.
Formation of the text summary. Next, let us
consider what can be done with the text, and the
inhomogeneous semantic network obtained from
it. Since notions – vertices of the semantic net-
work for a specific text – are ranked by their se-
mantic weights in the analysis, we can use this to
identify the portion of the sentences most signifi-
cant for the text. We can calculate weight charac-
teristics of the text sentences as a sum of weights
for notions included in the sentence. Further, we
can remove sentences, weights of which exceed a
predetermined threshold. We will obtain a quasi-
summary of the text. The cohesion of the text may
be broken, but sentences contained in it will bear
the meaning of the text.
Formation of asterisk chains. Separate sen-
tences of the quasi-summary and the correspond-
ing extended predicate structures describe separate
fragments of the situation. The extended predicate
structure has a corresponding (after the above
transformation) asterisk d = j <ci ri cj>. Then a
chain of extended predicate structures contains the
meaning of the quasi-summary:
( 1, )iD = d | i = N . (11)
where N is the number of sentences in the quasi-
summary.
Example of the pragmatic analysis of the text
Let us consider an example of pragmatic analy-
sis of the text involving the described above
mechanisms that allows identification of predicate
structure chains for sentences of a text essential
for representation of the text meaning. To simplify
the interpretation of the chain, only most impor-
tant parts will be taken from the extended predi-
cate structures: (subject-predicate-main object).
As an example of an extended predicate struc-
ture of a sentence we take a sentence from T.I.
Trofimova’s textbook “Physics course”, Moscow,
“High School”, 2001:
”Mechanics is a branch of physics that studies
laws of mechanical motion and reasons that cause
or change this motion”.
We will not describe in great depth the details of
the linguistic mechanism for extraction of the ex-
tended sentence predicate structure. Let us show the
final result. The only remark is as follows: the sen-
tence is broken down into simple components “Me-
chanics is a branch of physics” and “Mechanics
studies laws of mechanical motion and reasons that
cause or change this motion”.
In the first part the extended predicate structure
is very simple: “Mechanics (subject) – is included
in (predicate) – physics (main object)”.
The frequency of occurrence, the frequency of
co-occurence are counted for the stems. After the
formation of a semantic network, the frequencies
of stems occurrence are converted into their se-
mantic weights that allows calculation of the se-
mantic weight of the sentences.
УСиМ, 2015, № 6 33
If sentences with weights less than the prede-
termined threshold value are removed from the
text, what remains is the quasi-summary of the
text, the fragment of which is shown in Table 1:
Table 1. Quasy-summary of the text (fragment)
Sentences of quasi-summary Semantic
weight
1
Newton’s first law: every material point (body)
persists in its state of being at rest or of moving
uniformly straight forward, except insofar as it is
compel to change its state by force impressed.
99
2
Newton’s first law is true for all reference frames,
and the frames with respect to which it is true are
called inertial reference frames.
97
3
An inertial reference frame is a reference system
with respect to which a material point, free from
external forces either remains at rest or moves uni-
formly and in straight line
99
The sentences of the quasi-summary reveal
their extended predicate structures, which form
the very chains (see Table 2) that characterize the
pragmatics of the text. For the purposes of sim-
plicity, below is a chain of only an essential part
of the predicate structures (subject-predicate-main
object). The other members of the extended predi-
cate structures are omitted.
Table 2. The chain of predicate structures of the text (fragment)
Subject Predicat Predicat
1 Point persists state
compel it 2
Force
change state
3 Law is true NUL
4 Frames are called NUL
5 Frame Is NUL
6 Point remains at rest, moves NUL
Conclusion
This paper describes an approach that combines
statistical and linguistic methods of text analysis,
and semantic and pragmatic processing of texts us-
ing the proposed approach is demonstrated on spe-
cific examples. Combined application of the fast
statistical algorithms of text processing, as well as
linguistic algorithms and knowledge bases in the
form of dictionaries of valencies make it possible to
obtain the semantic representations of the whole text
with accuracy typical for the linguistic approach.
The understanding of the text pragmatics proposed
in the paper is not, in general, universally accepted.
However, such representation is sufficiently con-
structive to implement real mechanisms for the
automatic text processing.
The works was performed within the research
“Study of the mechanism of associative links in hu-
man verbal and cogitative activity using the method
of neural network modeling in the analysis of textual
information” (with financial support from the Rus-
sian Foundation for Basic Research, grant 14-06-
00363).
1. Leontyeva N.N. Avtomaticheskoe ponimanie tekstov.
Sistemy, modeli, resursy – M.: Academia, 2006.
2. Kharlamov A.A. Nejrosetevaya tekhnologiya predstav-
leniya i obrabotki informatsii (estestvennoe predstav-
lenie znanij). – M.: Radiotekhnika, 2006.
3. Kharlamov A.A., Raevsky V.V. Networks constructed of
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© А.А. Харламов, Т.В. Ермоленко, 2015
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/NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.)
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/ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
>>
/Namespace [
(Adobe)
(Common)
(1.0)
]
/OtherNamespaces [
<<
/AsReaderSpreads false
/CropImagesToFrames true
/ErrorControl /WarnAndContinue
/FlattenerIgnoreSpreadOverrides false
/IncludeGuidesGrids false
/IncludeNonPrinting false
/IncludeSlug false
/Namespace [
(Adobe)
(InDesign)
(4.0)
]
/OmitPlacedBitmaps false
/OmitPlacedEPS false
/OmitPlacedPDF false
/SimulateOverprint /Legacy
>>
<<
/AddBleedMarks false
/AddColorBars false
/AddCropMarks false
/AddPageInfo false
/AddRegMarks false
/ConvertColors /ConvertToCMYK
/DestinationProfileName ()
/DestinationProfileSelector /DocumentCMYK
/Downsample16BitImages true
/FlattenerPreset <<
/PresetSelector /MediumResolution
>>
/FormElements false
/GenerateStructure false
/IncludeBookmarks false
/IncludeHyperlinks false
/IncludeInteractive false
/IncludeLayers false
/IncludeProfiles false
/MultimediaHandling /UseObjectSettings
/Namespace [
(Adobe)
(CreativeSuite)
(2.0)
]
/PDFXOutputIntentProfileSelector /DocumentCMYK
/PreserveEditing true
/UntaggedCMYKHandling /LeaveUntagged
/UntaggedRGBHandling /UseDocumentProfile
/UseDocumentBleed false
>>
]
>> setdistillerparams
<<
/HWResolution [2400 2400]
/PageSize [612.000 792.000]
>> setpagedevice
|