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Discovery of role functions of persons on the basis of
knowledge structures
Выявление
ролевых функций лиц на основе структур знаний
Igor P. Kuznetsov (igor-kuz@mtu-net.ru)
Institute for Informatics Problems of
the Russian Academy of Sciences, Moscow
Кузнецов Игорь Петрович
Аннотация
Рассматривается семантико-ориентированный лингвистический процессор,
извлекающий из текстов естественного языка информационные объекты, их свойства
и связи и формирующий на этой основе структуры знаний. Одно из направлений
развития таких процессоров связано с выявлением имплицитной информации, которая
рассматривается в узком плане - как выявление новых свойств объектов, заданных
в неявном виде. Предлагается методика такого выявления, основанная на анализе
структур знаний. В качестве примера рассматривается выявление ролевых функций
фигурантов на базе их описаний в сводках происшествий.
Introduction
One of the primary tasks in the area of cognitive technologies is the
automatic extraction of knowledge from natural language (NL) texts. It is a
complex problem connected with developing linguistic processors which perform
automatic formalization of texts, i.e. mapping the texts into formal models or
knowledge structures. It should be noted that a lot of relevant information in
NL texts is presented in a concealed form. This information is called
implicit. An example is the task of
assigning certain features to persons on the basis of actions performed by
them. In the subject area of “Criminology” it is assigning such features as
“victim”, “suspect”, etc. to persons.
The methods of implicit information extraction should be considered in
the context of the knowledge extraction task, and it is conditioned by the
specific features of the linguistic processor. The given paper describes these
methods within the framework of the object-oriented linguistic processor
developed at the Institute for Informatics Problems of the Russian Academy of
Sciences (IIPRAS).
1.
The object-oriented linguistic processor
The research direction connected with unstructured NL texts processing
has been developing for 20 years at the IIPRAS for particular application areas
and specific user tasks [1,2]. One should consider
that the large category of users have the specific official responsibilities,
and respectively, constant interests. Completely concrete information is
necessary for them. For example, a criminal inspector seeks to extract
information on important figurants, their places of residence, telephones,
criminal events, dates and other such facts [3]; a personnel manager is
interested in the organizations, when and where a person worked and in what
position [4]. Other people try to fish out from the media the information about
the countries, important persons, catastrophes, places of interest and
historical monuments [5]. We call this concrete information interesting for a
user information objects.
Objects are distinguished by their types.
Let us note that the connections between
the objects, which interest users, can have the high degree of variety. For
example, not only the connection of the persons with their information from their
questionnaires or the objects can present interest, but also the actions or the
events, in which these persons participate. Such events are attached to the
time and the place. Moreover, some events can be a component part of others.
They can be connected with cause-effect and temporary relations. For the number
of problems similar connections play an important role. They also must be
revealed and processed. Therefore one should consider that events are also
information objects, interconnected and connected with other information
objects. Complex structures appear. For their representation within the
framework of the projects of IIPRAS (Russian Academy of Science) the language
of the extended semantic networks (ESN) has been developed, while for the processing
the production rules the language DEKL [6] has been implemented.
The ESN networks are represented in the
form of special graphs [6]. In the formal record they are the extension of the predicate
logic language. ESN consist of elementary fragments, each of which has its
unique code (see Section 3), which can stand at the argument places of other
fragments, and provide great possibilities for the representation of knowledge
structures. The language DEKL is designed for the transformation of such
structures. The problem of extracting knowledge from natural language texts is considered
from the point of view of developing information objects and connections with
the construction of the knowledge structures on the basis of which the solution
of user problems is achieved. For this within the framework of the IIPRAS projects
the object-oriented
linguistic processor (LP) converting the natural language (NL) texts to the
knowledge structures is developed and constantly updated. The processor LP
achieves a deep NL text analysis with bringing of synonymous groups to one
form, development of objects and their properties, identification of objects,
elimination of ambiguities, development and unification of various forms, which
present events or actions (including forms with the verbal noun, participial and
verbal-adverbial constructions), which are connected with the time and the
place [2-6]. As a result the structures of knowledge in the ESN formalism are created.
The linguistic processor (LP) is
realized by means of the language DEKL and is controlled by the linguistic
knowledge (LK) in the form of object dictionaries, means of parametric tuning,
and also the rules of extracting objects and connections [2,4,5,6]. With the
aid of LK the tuning of LP to the appropriate categories of users and text
corpora is accomplished. Concrete realization appears as a result. Thus, the paper
deals with the means of constructing a class of processors with powerful mechanisms
for their tuning and updating. Further development of such processors (LP) is
connected with the development of implicit information [7], which we will
consider in a narrow plan, i.e. as the addition of the structures of knowledge
by the new information, which is absent or assigned implicitly. In this article
the procedure of this development is proposed, which consists in the use of LP
for mapping NL texts onto the structures of knowledge (ESN) and the use of the means
of logical-analytical processing (productions of the language DEKL) for the
creation of new information.
Advantages and deficiencies of the
proposed procedure will be examined on specific objectives from the area of
“criminology”, that is the role functions establishment for the persons (participants)
on the basis of the acts performed by them or due to the participation in some specific
events. We consider the problem of assignment of properties to the persons (basing
on their participation in the acts of different kinds) - “the suffered”, “the
suspect” and others, if an explicit description of such properties is absent
from the text. For example, if it is said in the text “suffered Ivanov I.I.”, then another task appears, i.e. extraction of
some property in the process of linguistic analysis and forming of the
corresponding fragments in the knowledge structure. In this article the
discussion will deal with LP, customized for the Russian language texts (NL), although
the possibilities of LP are wider. There is a sufficient test of tuning LP to
the English language texts [9,10].
2. The choice of method
The
task of the role functions establishment for the information objects is a special case
of the more general task, connected with the estimation of objects according to
their descriptions in the NL texts, for example, with the estimation of the
stability of enterprise (according to the information from the Internet), by featuring
political figures (positive or negative depending on the statements in the
press), by the estimation of the role functions quality of product (basing on
the statements of users) and so forth. Quite frequently, it is not said directly
whether something is
bad, or good. As a rule, in NL texts the events are described, the situations,
in which one or other information object participated.
On the basis of them the estimation is done, which is often represented in the
form of a new (generated) property of object.
For the solution of this problem
different methods are used [11-15]. The most common one is the method of the
new properties of objects development by using the syntactical-semantic forms. For example:
<what-medicine> caused
allergy in <who-human organism>…,
<
what-medicine > has side effects …
<who-person> made scandal… and
so forth.
The application of such forms to the NL texts
consists in the search for “estimating” or “characterizing” words (of type
“scandal”) or for word combinations of the type “caused allergy” (“it can cause
allergy”), it "has side effects” (“side-line actions”), “to make scandal”
(“to brawl”)… And then the environment is analyzed, i.e., the words, which
stand to the left and to the right, their semantic classes (objects are
recognized by them) and case forms. Estimations of information objects as a
result are given. By the first two forms the “quality of medicines” is
estimated, while by the latter it is recognized that a man performed “hooligan
actions” or that he is “suspected”. It is known that in NL many versions are
possible for expressing the same idea - with the aid of different syntactic
constructions, verbal groups, forms and so forth. Therefore the number of estimating
word combinations will be sufficiently large. Moreover, the application of such
forms requires different forms of analysis - morphological (in order to reduce
different word forms to one form), syntactic (the trees are built of the
selection of sentences in order to isolate the connected components and to find
place for the estimated words) and semantic (in order to extract the objects,
which are evaluated). The use of syntactical-semantic forms is connected with certain
difficulties caused by special NL features: by the presence in texts of participial,
verbal-adverbial constructions, different explanations, facultative components
(time, place, purpose), anaphoric references and other
language structures. As a result, information objects are frequently disconnected
from the estimated words. Hence - the significant losses, which influence the
quality of estimation.
Example 1 (the text is taken from the summaries
of incidents of the City Office of Home Affairs, Moscow):
…
Gorelov Peter Sergeyevich,
01.03.76 yr/bir, liv: c.
Moscow, st. Young Leninists, h.71-6-12, does not
work, 01.02.1998 yr. at
Пример
1 (текст взят из сводок происшествий ГУВД г.
Москвы):
… Горелов Петр Сергеевич,01.03.76 г/р,
прож: г.Москва, ул.Юных
Ленинцев, д.71-6-12,не работает, 01.02.1998 г. в 14.30 у своего дома из
хулиганских побуждений в состоянии алкогольного опьянения учинил скандал и разбил оконное стекло в
квартире Литвиновой Галины Ивановны,20.07.1961 г/р, …
In this example the estimating
(characteristic) words are "made scandal” and "broke the window
glass”, they are located at a significant distance from the estimated person -
“Gorelov Peter Sergeyevich”.
This limits the possibilities of applying the forms. It is required that the
initial extraction of components, which must not be considered in the forms:
the years of birth, addresses, specific properties (“he does not work”, “in the
state of alcoholic intoxication”), time, place and others, which requires
sufficiently deep text analysis with the extraction of objects, their
properties and attributes. In connection with the aforesaid, another more
promising method is represented - when evaluation is accomplished at the level
of knowledge structures. For their construction the objective-oriented LP is
used producing the structures of knowledge in which the objects are directly connected
with the events and the actions and excluding the above mentioned losses. For
the development of implicit information (role functions of objects) the rules
of the DEKL language are used which analyze the structures of knowledge (ESN)
and form new properties of objects. In this case the structure of knowledge
does not change, but it is only supplemented by new (useful) fragments.
3. Meaningful portraits of documents
Within the framework of the proposed
procedure the development of the role functions of objects (implicit
information) is achieved at the level of the structures of knowledge, called
the meaningful portraits of documents (SS-documents). Let us examine how such
structures appear in the ESN formalism [2,3,6].
Example 2 (translation of the Russian text given
below). Text N22 is taken from the summaries of
incidents of the City Office of Home Affairs, Moscow:
01.02.98 yr. 16-30 to the Home Office applied citizen Mitrofanov Victor Mikhaylovich,
1955 yr. bir., liv.: Bohr
Highway 38-211, n/w. he stated that 01.02.98 yr. at 10-
Пример 2.
Текст документа (с номером 22) взят из сводок ГУВД:
01.02.98 г. в 16-30
в ОВД обратился гр-н Митрофанов Виктор Михайлович,
The objective-oriented LP performs the deep analysis of the text and
automatically builds its meaningful portrait (SS- document, transliterated):
DOC_(22,
“1-02-
OVD_(OVD/1+)
FIO(MITROFANOV], VICTOR,
MIKHAYLOVICH, 1955/2+) UNEMPLOYED (2-/3+) 3- (22, PROP)
ADR_(Borovskiy, Sh., 38,211/4+)
PROZH. (it
is 2nd, 4)
ADR_(UL,
FEDOSINO, HOUSE, 3/5+)
FIO (" " , " " , " " , NESKOLKO/6+)
UNKNOWN (6)
DRUNK (6-/7+) 7 (2,
PROP_)
SCANDAL (6, PYANYY/8+)
IS EIGHTH (22, ACT_)
TO REPORT (IT IS 2ND,
8-/9+) 9 (22, ACT_)
DATA_(1998,02,
~01, " 10-00" /10+)
When (9, 10)
TO TURN (1, GR- N,
2-/11+) 11- (22, ACT_)
DATA_(1998,02,
~01, " 16-30" /12+)
When (11-, 12-)
EXPRESS (6,
UNQUOTABLE, [BRAN]/13+) 13- (22, ACT_)
TO SET (6,
[SOBAKA]/14+) 14 (0, ACT_)
TO TURN (IT IS 2ND,
IN, [TRAVMPUNKT]/14+) 14 (0, ACT_)
TO PLACE (DIAGNOSIS,
BITE, [NOGA]/16+) 16 (0, ACT_)
PREDL_(22,11-, 4, 3-, 9, 13-, 14-/17+) 17- (2,15,341)
PREDL_(22,15-, 16-/18+) 18- (6,342,448)
For the original Russian text the automatically
generated SS- document looks as follows:
ДОК_(22,“1-02-
ОВД_(ОВД/1+)
FIO(МИТРОФАНОВ,ВИКТОР,МИХАЙЛОВИЧ,1955/2+)
БЕЗРАБОТНЫЙ(2-/3+) 3-(22,PROP_)
АДР_(БОРОВСКИЙ,Ш.,38,211/4+)
ПРОЖ.(2-,4-)
АДР_(УЛ.,ФЕДОСЬИНО,ДОМ,3/5+)
FIO(" "," ","
",НЕСКОЛЬКО/6+)
НЕИЗВЕСТНЫЙ(6-)
ПЬЯНЫЙ(6-/7+)
7-(2,PROP_)
СКАНДАЛ(6-,ПЬЯНЫЙ/8+) 8-(22,ACT_)
СООБЩИТЬ(2-,8-/9+) 9-(22,ACT_)
ДАТА_(1998,02,~01,"10-00"/10+)
Когда(9-,10-)
ОБРАТИТЬСЯ(1-,ГР-Н,2-/11+) 11-(22,ACT_)
ДАТА_(1998,02,~01,"16-30"/12+)
Когда(11-,12-)
ВЫРАЖАТЬСЯ(6-,НЕЦЕНЗУРНЫЙ,БРАНЬ/13+) 13-(22,ACT_)
НАТРАВИТЬ(6-,СОБАКА/14+) 14-(0,ACT_)
ОБРАТИТЬСЯ(2-,В,ТРАВМПУНКТ/14+)
14-(0,ACT_)
ПОСТАВИТЬ(ДИАГНОЗ,УКУС,НОГА/16+) 16-(0,ACT_)
ПРЕДЛ_(22,11-,4-,3-,9-,13-,14-/17+) 17-(2,15,341)
ПРЕДЛ_(22,15-,16-/18+) 18-(6,342,448)
A meaningful portrait consists of the elementary fragments, arguments of
which are words in the normal form (necessarily for the search and processing).
Each elementary fragment has its unique code, which is written in the form of
the number with the sign + and is separated by a slash line. For example, in
the fragment OVD_(OVD/1+) the sign 1+ is its code (but
1 is the reference to it). Fragments DOK_(22, “1-02-98.TXT”, “SUMMARY; ” /0+) 0 (RUS) indicate that
the meaningful portrait is built on the basis of the Russian-language text of
document with number 22 of the file of 1-02-98.TXT”, which was processed as the
summary of the incidents (linguistic knowledge depend on this). The following fragments
present police department OVD_(… /1+), person’s surname, name and patronymic FIO
(… /2+), person’s specific property UNEMPLOYED (2-/3+), address ADR_ (… /4+)
and so forth; the signs 2+, it is 2nd, 3+, 3-,… are the codes of the fragments,
with the aid of which their connections and relations are assigned. For
example, the fragment PROZH (live) (it is 2nd, 4) represents the relation that the
person (represented as FIO with code 2+) lives at the address (fragment [ADR]_ with code 4+). Actions are represented in the form of fragments
of the type SCANDAL (6, PYANYY/8+) it is 8 (22, ACT_), where it is represented
that “person (FIO with code 6+), being drunk, made scandal”. With the aid of it
is the fragment 8_(22, ACT_) indicates that the first
fragment is SCANDAL (…./8+) presents the action and relates to the document
with the number
Analyzing this example, it is possible
to make the following conclusions: 1) In SS- document the estimating (characterizing)
words occur either in one fragment with the object - SCANDAL (,…), or the next
one, i.e., the codes of the actions, in which the object participates, are
nearby in PREDL_(… 9, 13-, 14…). In this case the possibility of composite
actions is considered. 2) On the actions, represented as SCANDAL (,…), it is possible to draw the conclusion that the
discussion deals with “that suspected”, and TO REPORT (,) - that the person is
“suffered” or “the applicant”. Such conclusions are easily arrived at with the
aid of the rules IF… THEN (productions) of the language DEKL, which are the
basis for the extraction of role functions. 3) The particular difficulties of
dividing the text into the sentences occur (in the old version). The reduction
“of n/r” (with the point at the end) was not understood as the end of a
sentence. 4) The linguistic processor (LP) correctly identified the pronoun “he”, and also it knew how to reveal the
participation of the subject (“unknowns”)
by the actions “to be evinced by unquotable
swearing” and “to set dog”, which
also characterize subject. At the same time the LP could not connect the action
“diagnosis was set” with the person -
“Mitrofanov…”
(the code is 2-nd). In this case an example proved to
be successful. Also the processor LP (with its linguistic knowledge - LK) was
developed for the tasks of the criminal police, connected with different forms
of the objective searches: the search for similar pparticipants
(addresses, and so forth), search according to the connections, precise search
for objects, for the search by signs and other identifiers. In this case the
analysis of some complex NL forms was not required, i.e. the cases of the
enumeration of the objects participating in the uniform actions (they are
described by one verb), the enumeration of the actions of one object and others
in contrast to the aforesaid, with the extraction of role functions for each
object the indication of its participation in each action is required. Hence it
follows that with the use of the proposed procedure the more qualitative extraction
of role functions is directly connected with the works on improvement of LP in
the aspect of the development of objects and their actions. In many instances the
numerous errors caused the inaccuracies in SS-document, e.g.: the absence of
punctuation marks or their presence (where it was not required), the inappropriate
reductions, gaps in the words and many others. The fact is that the documents,
entering the summaries of incidents, are composed on the spot by people
(militiamen) of different degree of literacy. Hence – the additional noise and
loss. Thus, meaningful portraits are the collections of fragments of ESN which
represent the sufficiently high level of formalization of NL texts and are
convenient for the working - with the aid of the instrument means - DEKL [3].
Besides LP which analyzes texts and builds SS-documents, there is a reverse
linguistic processor (LP) which on the basis of the fragments of the SS-
document generates the NL texts presented to the user [6].
4. The means of the development of the
role functions
As it has already been said, within the
framework of the proposed procedure (instead of the application of
syntactical-semantic forms to the documents) the rules are used for logical
conclusion and transformation of the knowledge structures - the SS- documents,
in which there are no morphological features (of type who, whom,…), and the subjects
and the objects are distinguished by their arrangement in the fragments of ESN,
which present actions. The names of fragments present the nature of actions.
Syntactical-semantic forms are transformed into the fragments of ESN which
determine conversions and logical conclusion achieved by productions of
language DEKL. Such fragments play the role of the logical-semantic shell,
which determines conversions and logical conclusion on the basis of SS-documents.
After filling of the shell by ontological-fragmental knowledge (OFK) which
consist of the mentioned fragments (ESN), the program is formed, which
accomplishes the development of role functions and completion of the SS-document
by the appropriate fragments. With this approach it is possible to avoid many
difficulties, connected with the design features of NL and the specific
character of the use of syntactical-semantic forms. There are many versions of
construction of the shells and representation of the corresponding knowledge which
are distinguished according to the degree of their generality. Let us examine
the version which is at present realized and verified.
Case 1. The role functions are determined by
the names of actions. In this case for the extraction of objects (participants)
which should be assigned properties (role functions), the fragments of the
following form are used :
INTERPRET (MAN_2, FIO, " suffered")
FORMA_CC (MAN_2, CLASS_D4, " ") CLASS_D4 (TO TURN, TO STATE, TO
REPORT, TO PASS AWAY,…)
The first fragment INTERPRET (…) means
that from the SS- document it is necessary to extract the fragments of the form
FIO (…), that correspond to participants, and to analyze the possibility of assigning
them the property "suffered". Such participants are conditionally
designated as MAN_2. The second fragment FORMA_CC (…) specifies the conditions
for assigning this property to MAN_2, determined by the constant CLASS_D4. In
the third fragment CLASS_D3 (…) the words are given which present actions. It is
represented that the words belong to the class CLASS_D3. If the participant
occurs in one of the enumerated actions, then to this participant the property
"suffered" is assigned. This participation is revealed via the
analysis of the SS-document. If there is a fragment TO TURN (…, it is n-th,…) in it, the argument of which is the code FIO (… /N+),
then the fragment N-("suffered") is added that represents the role
function of the corresponding participant. Conformably for the SS- document
represented in example 2 the analysis will occur as follows. Consecutively the
extraction of fragments FIO (…) corresponding to the participants is performed.
First FIO (MITROFANOV,… /2+) will be extracted . Its
code is 2- is the argument of the fragment TO TURN (1, GR- N, 2-/11+), that
presents the action. In connection with this to SS- document the fragment 11-
("suffered") will be added, which via the reverse LP will be transformed
into the statement that “Mitrofanov Victor Mikhaylovich is a suffered person”. These actions are realized
within the framework of the logical-linguistic shell.
Case 2. Role functions are determined by the actions
and elucidating words. For this the same fragments are used, as in the first
case, but during the enumeration of the names of actions the additional
fragments which present actions with the possible elucidating words, are
introduced:
INTERPRET (MAN_1, FIO, “suspect”)
FORMA_CC (MAN_1, CLASS_D3, "
")
FRAUD (USER, POKUPATEL/15+)
TO SET (SOBAKA/16+)
TO BE EXPRESSED (UNQUOTABLE, SWEARING, MATERNYY,… /17+)
CLASS_D3 (IS DELAYED, TO BE SOUGHT,…, 15,16-, 17-)
The given fragments determine actions of
the extraction of persons (MAN_1), by which the property of “suspect” is
assigned. For this at the level of the knowledge structures their participation
is analyzed in the actions “is delayed”,
“to be sought”, and also in the
composite actions: “to set dog”, “to be expressed unquotable…”, “to be expressed by swearing…” and others.
In example 2 the code of fragment FIO (" " ,
" " , " " , NESKOLKO]6+), that represents the unknown
persons is the argument of the fragment TO SET (6, SOBAKA/14+), representing
action “to set” with the elucidating
word “dog” – “sobaka”.
Therefore the fragment 6 is added ("suspect"),
that represents that “the unknown persons
are suspected”, and through the reverse LP the explanation to this
conclusion is offered, see below. A similar conclusion will be made on the
basis of the fragment TO BE EXPRESSED (6, UNQUOTABLE, BRAN/13+), but with other
explanations.
Case 3. The actions determine the role
functions of several persons. For this (additionally to the fragments
INTERPRET) the fragments are added: CLASS_D1 (TO STRIKE, TO BEAT UP,…) FORMA_CC (MAN_1, CLASS_D1, MAN_2), where FORMA_CC (…) indicates
the need of the search of two persons - "suspect" and "suffered"
(MAN_1 and MAN_2), that participate in one action, which are mentioned in the
fragment CLASS_D1 (…). For example, “certain
person struck another…”. In the appropriate
fragment TO STRIKE (…) the code FIO (…) that corresponds to the first person
will stand in front of the second. The given fragments of ESN compose the
knowledge OFK which are constantly supplemented - due to the filling of classes
by the new words-actions and with the elucidating words. The process of filling
is sufficiently simple. If role function is not revealed, then it is necessary
to look in the SS- document in which the action of one or another participant (by
the text its role is easily determined) occurs. Further, the corresponding
constants are located, by which are supplemented the classes of knowledge OFK. Subsequently
it is intended to automate the process of completing the knowledge OFK as
follows. In the text the words, which determine role functions, are noted.
Further, in the formed SS-document the corresponding constants which supplement
knowledge OFK are located.
5. Explanation of the results
The explanation of results is accomplished
through the reverse LP which on the basis of SS- document and additional
fragments builds texts in natural language which are displayed to the user. The
reverse LP through the codes of the fragments which correspond to object and
actions, finds the sentence (PREDL]) and its location in the text of a document.
Through the arguments of these fragments (the words in the normal form) the
processor finds the components of the sentences in which the mentioned object
and actions are described. These components are converted into the form
suitable for the delivery to the user. The fact is that many components are
transformed depending on the context. For example, “… he threatened Petrov I.I..…”
– “…ugrozhal Petrovu I.I….” , where during the delivery FIO “Petrovu” should be transformed into “Petrov I.I.” Further the description of the object is delivered, its
generated property and actions which explain this property - role function.
Example 3. with the use of the above given
knowledge OFK of the SS - document of example 1 the role functions will be generated
which with the aid of the reverse LP will be given out to user in the following
form:
unknowns - suspected,
since - unknowns, being in the drunk state
made scandal,
they expressing themselves by unquotable
swearing, they set dog
Mitrofanov Eugene Mikhaylovich,
1953 r. - suffered,
since – applied to OVD citizen Mitrofanov Eugene Mikhaylovich,
1953 yr. birth,
since Mitrofanov applied
to trauma care centre.
It should be noted
that all the actions considered in the paper connected with various cases of
role functions establishment and explanations of the results are implemented
within the framework of the logical semantic environment written in the logical
programming language DEKL. Since the DEKL language is oriented at the
processing of knowledge structures (represented in the form of the extended
semantic networks – ESN) and since it features the generalized production rules
[6], the program code in the DEKL language is very simple and concise: it comprises
16 productions and about 4 Kbyte of text.
Conclusions
The proposed procedure
of the role functions extraction centered at the analysis of knowledge structures
is sufficiently promising from the point of view of the knowledge bases technology
development. The current task is to improve its performance for the documents comprising
enumeration of the type: 1. Ivanov I.I.… 2. Petrov A.A.…. 3.… and
further follows the continuation, which describes their acts, for example, “were subjected to detention by…” or “who performed…”. The recognition of such
cases requires further upgrade of the linguistic processor (LP) software. The
quality of analysis is lowered by the breaks in significant words of the type “Iva nov” or “Iva-nov”,
which are typical for the summaries of incidents. The methods were tested on
the basis of the summaries of incidents which contain about three thousand
documents (each document consists of 10 - 80 lines). In the case of the summaries
processing the documents with the mentioned enumerations (there were about 10%
of them) were withdrawn and in the remained texts the gaps in the words were
removed. At the current moment the program which realizes the proposed
procedure gave about 80% of correct recognition of role functions, and about
65% of complete explanations with the indication of all acts. But these numbers
rapidly change for the better due to the means (the LK and OFK knowledge) of tuning
the LP to special features of the subject area texts. For this not much time is
required. Let us note that tuning itself to the extraction of the role
functions of persons from the mentioned summaries (with reaching the indicated
percentages), required about two weeks of the work of one person. The development
and fixing of the shell itself took about four days. The subsequent development
is connected with the improvement and the tuning of LP to the work with complex
NL forms. At present the extraction of actions is interfered with causal word
combinations of the type “out of the
hooligan motives”, “owing to the
hostile relations” and so forth, which at present are introduced into the
system. Difficulties appear with the transfer of the subject of action to other
actions to which the subject is not assigned explicitly, but its presence is
implied.
The second direction
of research and development is connected with the extension of the shell features
to the solution of other problems connected with the estimation of objects
depending on the nature of statements about them in the texts of description. Within
the framework of the studies conducted it is also intended to tune the shell to
the work with the English language texts. Since the meaningful portraits of the
English language and Russian language texts have the identical structure (SS-documents),
this tuning cannot be labor-consuming.
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