Linguistic and algorithmic aspects of object extraction from natural language texts

 

 

 

Abstract A semantic linguistic processor which extracts the objects and their links from natural language texts is considered. The paper analyzes the experience of using the processor for formalization of texts in various subject fields: criminal actions, mass media, terrorist activities (in Russian and English). Peculiarities of the texts are taken into account by linguistic knowledge of the processor: the system can be tuned to various subject areas. We describe the use of this processor for text formalization in different subject areas, such as criminology (summary of incidents, accusatory conclusions, etc.), THE MEDIA (documents about terrorist activities), personnel management (autobiographies, resume). Special features of each problem area are examined: the collections of extracted objects, the means for their identification, their connections, occurring contractions, punctuation and special signs, specific character of language constructions, etc. – all these special features were taken into account in the linguistic knowledge development.

Keywords: semantics, natural language, linguistic processor, knowledge engineering, data extraction

 

1 Introduction

 

A tremendous increase of the documents flow, obtained by the users through different information channels (including the Internet), requires new solutions. The large part of such documents exists in the form of natural language texts (NL). In many cases one cannot read and comprehend even the smallest portion of the factual information available. The existing information means can render assistance, but for this a preliminary formalization is required. At the same time the majority of end users are people interested in specific subject things. For example, a criminal inspector seeks to extract information on important figurants, their places of residence, telephones, criminal events, dates and other such facts; a personnel manager is interested in the organizations, when and where a person worked and in what position. Other people try to fish out from the media the information about the countries, important persons, catastrophes, etc. We call this concrete information interesting for a user information objects.

Hence follows the need for constructing a new class of information systems, which would consider the interests of the end user and be oriented at extracting information objects from texts [1-3]. At present this problem is in the focus of attention of many researchers and developers [4-19 ].

In this article a class of such systems is presented, based on the use of special linguistic processors (LP) and technology of knowledge bases (KB). Linguistic processors are necessary for the deep processing of texts with the development of information objects and connections. On the basis of the latter the structures of the knowledge comprised in the knowledge base are formed. We call such processors semantics-oriented. Their special feature is the employment of the linguistic knowledge (LK), organized in such a way as to consider lexical and semantic special features of natural language with the formation of the knowledge structures [1,14 ]. At the level of KB it is possible to consider more fully the needs of the users for decision of the following tasks.

First, due to the use of the reverse linguistic processors the formation of reports, filling the required table forms and relational databases have become possible.

Second, due to the support of the expert component, it is possible to ensure the updating of the information by the analytical results, obtained via processing the knowledge structures.

Third, intelligent features are provided due to the organization of different types of search:  the search for concrete objects, the search for similar objects, the search for connections, etc. Such forms of search relate to the "semantic" facilities, since the results are achieved not at the level of words or word forms, but at the level of the knowledge structures from KB. We call the systems of this type semantics- oriented.

During the last fifteen years on the basis of the studies conducted at the Institute for Informatics Problems of the Russian Academy of Sciences the semantics- oriented systems and linguistic processors have been developed for the formalization of natural language texts and their analytical processing for different subject areas: criminology (summary of incidents, accusatory conclusions, etc.), the Media (documents about terrorist activities), personnel management (autobiographies in the Russian and English languages). These are integrated systems DIEZ, IKS, "Analyst, "Criminal", Lingua-Master [12-17].

This paper presents a discussion of special features of these systems, the linguistic processors and knowledge bases employed in them determined by the tasks and specific character of natural language.

 

2 Criminology information objects

 

The flows of documents in the criminal police comprise the summaries of incidents, information on the criminal cases, accusatory conclusions, etc. In these documents much concrete information is contained which concerns figurants, their acts, the instruments of crime and other facts. The basic tasks are different forms of search. Note that monthly accumulated volumes of new information of this type comprise tens and hundreds of megabytes. No one can read all this and hold it in the head. The full-text data bases do not solve this problem, since working with the NL texts they produce much noise (excessive documents) and significant loss of information. The reason for this is a special feature of the Russian language: the free order of words. The words relevant for the query can be scattered in the text of a document and relate to different entities. For eliminating these deficiencies the criteria of words proximity are introduced, they cut the endings of word forms (normalization process) and carry out the indexing of the normalized words, however, this does not radically solve the problem.

Another approach is the use of relational data bases. But for this the labor-consuming work of specially trained people is required on formalization of NL texts: extraction from the documents (incident descriptions) of persons, addresses, dates... and filling the corresponding tables in a data base. It is extremely difficult to make this with the large flows of documents.

For this task the system "Criminal" was developed at the end of the 90-ies [12,13]. Its special feature is automatic analysis of text with the extraction of the necessary collection of information objects. The "Criminal" system was verified on 500 thousand incidents from the summaries of Moscow Criminal Police Office (GUVD), and it showed the unique results on the basic objects: coefficient of noise (excessive words in the objects) was not more than 1-2% and losses were not more than 3%.

The following basic objects must be singled out (with minimum loss):

• persons (by family name, given name and patronymic - FNP) with their role features (criminal, victim);

• the verbal description of the persons, their distinctive signs;

• address, posting information attributes;

• date(s) mentioned;

• weapon with its special features;

• telephone numbers, faxes, e-mails with their subsequent standardization;

• the means of transport with the indication of the vehicle type, its state number, color and other attributes;

• passport data and other documents with their attributes;

• explosives and narcotic substances;

• police departments;

• the police officers.

Secondary objects (their loss is less fatal):

• organizations;

• positions;

• quantitative characteristics (how many persons or other objects participated in an event);

• the numbers of accounts, sums of money with the indication of the currency type.

Connections:

• event (criminal, terrorist, breakdown of articles and so on) with the indication of the information objects participation in them;

• time and the place of events;

• the connection between different types of information objects (with whom a person works in an organization, or lives at the same address, in what events participated together with other objects, etc.).

Some difficulties of the objects extraction from texts consist in the following. First, the difficulties, connected with the special features of the Russian language. These are the free order of words, the presence of homonymy and polysemy, the variety of language forms for expression of one and the same meaning (synonymy). For example, any event can be expressed with the aid of the verbal forms, verbal nouns, participial constructions, etc. they must be reduced to one form.

Second, the presence (especially in the summaries of incidents) of a large number of reductions, which must be deciphered via the analysis of context. For example, g. can indicate YR, CITY, STATE. and other.

Third, there are many omissions. For example, after a figurant the address is written, year of birth and other data. They must be connected with the figurant.

An important task is the identification of objects (figurants) in the entire text, the use for these purposes of indicative pronouns, brief names, anaphoric references. This is especially necessary for the accusatory conclusions (verdicts), where one and the same person is mentioned repeatedly (by different methods of naming) throughout the entire document. Taking into account the difficulties and in accordance with the tasks the linguistic processor of the "Criminal" system was developed, which achieves normalization of words, their grouping with the formation of units, the identification of objects and the establishment of connections. As a result for each NL document a semantic network called the meaningful document portrait was constructed automatically. The latter are the knowledge structures of the knowledge base which serve the basis for implementing different forms of semantic search : the search by features and connections, the search for the objects connected at different levels, the search for similar figurants and incidents, the search by distinctive signs (with the use of ontologies).

The expert component is supported for the classification of incidents by the catalogs of the criminal police: the "form of crime", the "method of the accomplishment of crime" and others. The result is introduced into the meaningful portrait. There is a complete set for tuning to the subject area.

 

3 Personnel management tasks

 

One of the key problems of personnel and recruiting agencies is connected with automatic processing of autobiographical data, claims for work (resume), written in a sufficiently arbitrary form, i.e. in the form of NL text. Such texts contain the following information about a person: family name, given name and patronymic of a person (FNP), year of birth, address, the time and the place of studies with the indication of the educational establishment designation, etc.

Their automatic formalization is required with extraction of information objects and their mapping into the fields of an assigned form or a web site. Then the use of database standard means for the solution of user tasks becomes possible. This formalization is done by hand in many agencies: by specially trained people, or by an applicant who is proposed to introduce the information into the indicated fields of the required form. This work is sufficiently labor-consuming.

The linguistic processor of the "Criminal" system was taken as the prototype for automation of these works. However, it was customized in accordance with the special features of the subject area [17].

First, this is the need for extracting another collection of objects and connections.

Second, their division into the groups is different. For example, the grouping of objects (organizations, dates and of others.) into those, which relate either to the studies or to the professional activity of a person.

Third, the need for using the expert systems for logical infer of the data stated implicitly. We refer to such data as expert objects.

 

Basic objects:

• a person, who composes thea claim (as a rule, at the very beginning of claim);

• the date of birth or age;

• e-mail;

• postal address;

• home telephone;

• cell phone;

• office telephone;

• personal Internet- page;

• the desired position;

 

STUDIES

 • the name of an educational institution;

• department (specialty);

 • diploma (degree);

• the beginning of studies (date);

• the end of studies (date);

 

PROFESSIONAL EXPERIENCE

• the beginning of work (date);

• the end of work (date);

• the name of organization;

• the held position;

• responsibility, function, achievement.

 

COURSES (instruction)

• the conducting organization;

• name of the course;

• diploma (certificate);

 • the beginning of the course;

• the end of the course.

 

Expert objects:

• gender;

• education (secondary, higher and other);

• professional area (according to the assigned classification);

• specialization (according to the assigned classification);

 • work experience (the number of years is summarized);

• the region (it is calculated from the address).

 

The extraction of a major part of these objects required only the modification of linguistic knowledge (LK). However, special features of the texts and the tasks decided required the enforcement of the linguistic processor facilities. This was caused by the following factors.

First, by the variety of the NL forms expressing the dates and time intervals. For example, dates can be in the contracted form (avg.05), in the form of fractions (09.99 g.), different kinds of special signs or quotation marks (09/99 or of 09'y999), etc. the intervals: 15.05-01.12.99 or May-June 06 and other variants. The difficulties caused by their confusion with the fractions, the absence of the keywords of the type g. (yr), etc. Moreover, one of the requirements was bringing the dates to the standard form – i.e. the interpretation of contractions.

Second, certain difficulties were caused by the tasks of grouping the objects into the types and composing the rules of their layout. For example, comparatively frequently in the resume such objects as organizations (where a person worked or studied), positions, periods of work and basic responsibilities are sequenced arbitrarily. If a time interval of work in any organization is recorded at the end and another organization is mentioned further, then it is necessary to know how to determine, where to assign this time interval. Time intervals, dates or other organizations (for example, the customers of a project) can stand, also, inside the text of the description of work, which causes additional difficulties. A human can easily understand what relates to what. But it is sufficiently difficult to design the formal criteria of separation and correlation, which would give a tolerable degree of noise and losses. For this objective special means were introduced in the linguistic processor, which, relying on dates (or organizations), performed a search for the objects connected with them.

Third, many users created their resumes on the basis of the documents, taken from different tables, forms. As a consequence, the absence of punctuation marks (periods), the presence of special signs, which remained after recoding of the text. All resumes (if there were no empty lines) were interpreted as one sentence. To overcome this the block of morphological-lexical analysis was supplied with the special means for tuning, i.e. the rule for separating the sentences. For example, if a word is a verb written with the capital letter and occupies the first position in a line, then this is the beginning of a sentence. There are many such heuristic rules including those which consider the role of special signs, separating symbols, etc.

Fourth, for obtaining expert data (objects) the expert systems (ES) were build into LP, which relate a document to a specific category (point of classifier) on the basis of the meaningful portraits analysis. Two types of shells for the ES are realized in the system. The first is based on the weight coefficients of the words, which correspond to the specific category. The second is based on the presence of words in the information objects.

In ES of the first type the words are connected with each category with the indication of their weights. Such weights are the result of the standard documents statistical analysis (analyzed by human), i.e., the stage of instruction is envisaged and machine learning methods employment.

In ES of the second type with each category the characteristic words or pairs of words (word combinations) are connected taken from the fragments which correspond to the information objects of the type indicated. One and the same word or word combination can be related only with one category.

And finally, the need for a reverse linguistic processor which would serve for converting the objects into NL components and mapping them into the fields of a form or a web site. This processor has its linguistic knowledge, with the aid of which the sequence of the headings (fields) delivery is assigned and the expectations with what objects they must be filled are specified. For extraction of such objects their names (ORG _, WORK _,...), and the connections, given in the meaningful portrait are used. For each selected object its description is constructed of the normalized words which constitute this object. Further, via the object its sentence is located. Due to the means of positioning the sentence place in the text is located. According to the description of the object in this interval a piece of sentence which corresponds to the object searched for. This piece is the resulting output.

 

4 Documents of the media on terrorist activities

 

The problem of information support for antiterrorist activities in the contemporary world is very acute and attracts the attention of researchers; however, the working knowledge extraction systems  for this field only begin to be created [18]. The principal task here is the extraction of the documents which relate to the terrorist activity from the flow of media communications, with the subsequent analysis of these documents. The linguistic processor of the "Criminal" system was taken as the prototype for automation of these works. It was developed in accordance with the special features of the subject area and the tasks. In LP the following information objects were additionally introduced:

• terrorist groups and organization (Terrorizm);

• participants of terrorist groups with the indication of their roles (leader, head of, etc.);

• the armed forces, assigned for antiterrorist combat (Military_.Force);

• time intervals (see Section 2).

We developed linguistic knowledge (LK) for the extraction of these objects. In accordance with the specific text characteristics LK was augmented by the new rules for the extraction of objects, for example, the extraction of the place of event in the forms such as "in 25 km from Kabul" or "the camp near Umma city" and so forth. The character of composite names with their elements of Abu (father), Ibn or Ben (son) was taken into account. For example, Abd ar-Rasul, Ben-Achmad. Accordingly, the FNP field became more complicated. For well-known terrorists the reduced names are used, as a rule, for example, Ben Laden (instead of Osama Ben Laden), Basayev (Shamil' Basayev), etc. Special means of their identification were introduced into the linguistic processor.

As in the previous cases, for extracting objects all versions of an object name including the brief form possible in the text were considered. Standard objects (FNP, dates, addresses, the forms of weapon and others) are reduced to one (standard) form. The identification of objects is performed taking into account brief designations (for example, separate surnames or names with FNP), anaphoric references (indicative and personal pronouns, for example, "this person", "it...") definitions and explanations (for example, "the mayor of Moscow Luzhkov" is identified with the subsequent words "mayor", " Luzhkov "). For the extraction of events and connections the analysis of verbal forms, participial and adverbial constructions is carried out.

At the same time the basic task of the LP use differed from the previous cases: this was the need for operation (as a separate module) within the framework of the integrated systems of information collection and processing. The exchange was conducted through XML- files [20]. For that end a reverse LP was developed, which constructs XML- files on the basis of meaningful portraits (see Appendix 1).

Thus, the input for the linguistic processor (LP) is a natural language text, and the output is an XML- file, where all chosen objects and connections with the indication of sources are represented. This LP named Semantix is provided in the form of an SDK- module. It works under WINDOWS, but it can be recompiled for the work under LINUX.

The Semantix Processor is an independent module and it can be used without the mentioned systems for the standard tasks of analytical services. There are means of tuning to the objects of other types - due to the linguistic knowledge or the dictionaries.

Let us give some explanations. Each object has the following structure:

  <OBJECT ID="7" TYPE="Organization">

    <ARG CONST="Headquarters />

    <ARG CONST="Residence" />

    ...

    <SOURCE> Headquarters residence of the opposing group</SOURCE>

  </OBJECT>      

where ID="7" – is an identification of an object, the TYPE="Organization" is its type. The text component corresponding to the object is also given. Objects relations and their participation in the actions are given through the REF=... references. For example, with the help of  the following construction 

  <ACTION ID="15" TYPE="Blow">

    <ARG CONST="At" />

    <ARG REF="7" />

  </ACTION>

where the sentence "one of the blows struck the headquarters of the oppositional group" is represented. For each object or action the reference to the sentence is given. The Semantix processor uses sufficiently universal constructions of XML- file: one object (through the reference) can include another object. Properties are given as arguments. If necessary the type of attribute is indicated.

For example, in the statement

<ATTR TYPE="YEAR" VALUE="2003"/>

the year is indicated, etc. An XML file has a complete set of information items necessary for the use in different integrated systems.

 

5 Conclusion

 

The Objective- oriented linguistic processors can be used in different areas of application where the extraction of useful information from natural language texts is required. In this case, the processors, described in this work, possess a number of essential advantages. The recently appeared systems such as Integro Ontos, Arion, etc. (as far as we know) extract only the objects of several types. As a rule, these are person, organization, date, address.

In the processors of the Semantix, Lingua-Master, “Criminal” systems up to 40 types of objects are extracted with high accuracy and minimum noise. For example, the system "Criminal" was verified on about 500 thousand incidents from the summaries of Moscow Criminal Police Department, and on the basic objects showed the unique results: the coefficient of noise, i.e. excessive words in the objects) is not more than 1-2% and losses are not more than 3%. The Semantix Processor was fixed on a smaller quantity of documents dealing with the terrorist activity, and therefore there can be more noise and losses in it. But this can be quickly fixed. The fact is that to consider everything which can be encountered in the NL texts is impossible. Therefore, in the first place, the representative collections of test documents are extremely important, and in the second place, the means of fixing or tuning of linguistic processors are as follows: the employment of hybrid approaches comprising hand-made rules and statistical means for rapid correction and fine adjustment of linguistic knowledge.

In our systems there is an entire complex of such means which ensure rapid tuning to the applications (including the introduction of new objects and connections) taking into account the demands of customers [19]. Note that in the mentioned processors the objects are brought to the standard form (for example, FNP, address, date) with the indication of the types of components. A sufficiently in-depth analysis of sentences is conducted with the development of verbal forms, and also with the identification of objects of the entire text. The analysis of the complex language structures is ensured: forms with  verbal nouns, participial and adverbial constructions, coordinated terms, etc. is supported by the expert component. The Semantix processor can be used as a stand-alone (independent) module [21]. At present the English language version of the object - oriented linguistic processor Semantix [15,16,19, 21 ] is being developed.

 

References

 

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[7] Popov, B. et al. KIM - A Semantic Platform for Information Extraction and Retrieval // Journal of Natural Language Engineering, 10(3-4), 2004, pp. 375-392.

 

[8] Doddington, G. et al. Automatic Content Extraction (ACE) program - task definitions and performance measures // Fourth International Conference on Language Resources and Evaluation (LREC), 2004.

 

[9] Han, J., Pei  Y. Yin, and Mao, R. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach,”  // Data Mining and Knowledge Discovery, 8(1), 2004, pp. 53–87.

 

[10] Dong, G. and J. Li. Efficient mining of emerging patterns: Discovering trends and differences //  Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and DataMining, S. Chaudhui and

D. Madigan, editors,  ACM Press, San Diego, CA, 1999, pp. 43–52.

 

[11] Kozerenko, E.B. Multilingual Processors: a Unified Approach to Semantic and Syntactic Knowledge Presentation. In Proceedings of the International Conference on Artificial Intelligence IC-AI'2001. H.R. Arabnia (ed.), Las Vegas, Nevada, USA, June 25-28, 2001. CSREA Press, 2001, pp.1277-1282.

 

[12] Kuznetsov I.P. Methods of Processing Reports with the Extraction of Figurants and Events Features // In Dialogue'99: Proceedings of the International Workshop "Computational Linguistics and its Applications", Vol.2, Tarusa, 1999.

 

[13] Kuznetsov I.P., Matskevich A.G. The System for Extracting Semantic Information from Natural Language Texts // Proceedings of the Dialog International Workshop "Computational Linguistics and its Applications", Vol.2, Moscow: Nauka, 2002.

 

[14] Kuznetsov I.P. Natural Language Texts Processing Employing the Knowledge Base Technology // Sistemy i Sredstva Informatiki, Vol.13, Moscow: Nauka, 2003, pp. 241-250.

 

[15] Kuznetsov, I., Kozerenko, E. The system for extracting semantic information from natural language texts // Proceeding of International Conference on Machine Learning. MLMTA-03, Las Vegas US, 23-26 June 2003, p. 75-80.

 

[16] Kuznetsov I.P., Matskevich A.G. The English Language Version of Automatic Extraction of Meaningful Information from Natural Language Texts // Proceedings of the Dialog-2005 International Conference "Computational Linguistics and Intelligent Technologies", Zvenigorod, 2005pp. 303-311.

 

[17] Kuznetsov I.P., Matskevich A.G. Semantics Oriented Linguistic Processor for Automatic Formalization of Autobiographical Data // Proceedings of the Dialog-2006 International Conference "Computational Linguistics and Intelligent Technologies", Bekasovo, 2006, pp. 317-322.

 

[18] Voss, S. and Joslyn C.A. Advanced Knowledge Integration in Assessing Terrorist Threats // LANL Technical Report LAUR 02-7867,  2002.

 

[19] Somin N.V., Solovyova N.S., Charnine M.M The System for Morphological Analysis: the Experience of Employment and Modification // Sistemy i Sredstva Informatiki, Vol. 15 Moscow: Nauka, 2005, pp. 20-30.

 

[20] Gardner, J. R.  and Z. L. Rendon, XSLT and XPATH: A Guide to XML Transformations, Prentice Hall, 2001.

 

[21] Web site with the demo version of  the Semantix system:  http://semantix4you.com

 

Appendix 1

 

Input Text:

12:16 27.12.2002 One of leaders of insurgents - Arabian Abu-Tarik isdestroyed in the Chechen Republic.  In the Chechen Republic one of leaders of Islam terroristic groupthe mercenary Abu-Tarik - assistant of Abu al-Valod, successor ofHattab, is destroyed. As has informed the Ministry of Foreign Affairsof the Chechen Republic, joint forces of Chechen special militia anddivisions of federal forces destroyed the insurgent in settlement StaryeAtagi of Groznensky region during the addressed check up.  In one of the houses there were found the hiding place with theconfidential Arabian documents, three sub-machine guns andgrenades, ammunition. There are no losses among the participantsof the operation.

 

     XML-file (Semantix output):
<?xml version=`1.0` encoding=`windows-1251`?>
<DOCUMENT DOC_NUM=`0`>

  <OBJECT ID=`1` TYPE=`Date`>
      <ATTR TYPE=`YEAR` VALUE=`2002`/>
      <ATTR TYPE=`MONTH` VALUE=`DEC.`/>
      <ATTR TYPE=`DAY` VALUE=`27`/>
      <ATTR TYPE=`HOUR` VALUE=`12`/>
      <ATTR TYPE=`MINUTE` VALUE=`16`/>
      <SOURCE> 12 16 27. 12.</SOURCE>
  </OBJECT>
  <OBJECT ID=`2` TYPE=`Terrorizm`>
      <ARG CONST=`1`/>
      <ARG CONST=`LEADER`/>
      <ARG CONST=`OF`/>
      <ARG CONST=`INSURGENT`/>
      <SOURCE> Leaders of insurgents</SOURCE>
  </OBJECT>
  <OBJECT ID=`3` TYPE=`FIO`>
      <ATTR TYPE=`SURNAME` VALUE=`ABU - TARIK`/>
      <SOURCE> Abu tarik -</SOURCE>
  </OBJECT>
  <OBJECT ID=`4` TYPE=`Place`>
      <ARG CONST=`CHECHEN`/>
      <ARG CONST=`REPUBLIC`/>
      <SOURCE> Chechen Republic</SOURCE>
  </OBJECT>
  <OBJECT ID=`5` TYPE=`Terrorizm`>
      <ARG CONST=`1`/>
      <ARG CONST=`LEADER`/>
      <ARG CONST=`OF`/>
      <ARG CONST=`ISLAM`/>
      <ARG CONST=`TERRORISTIC`/>
      <ARG CONST=`GROUP`/>
      <SOURCE> Leaders of Islam terroristic group</SOURCE>
  </OBJECT>
  <OBJECT ID=`6` TYPE=`FIO`>
      <ATTR TYPE=`SURNAME` VALUE=`ABU`/>
      <ATTR TYPE=`NAME` VALUE=`AL-VALOD`/>
      <SOURCE> Abu al Valod</SOURCE>
  </OBJECT>
  <OBJECT ID=`7` TYPE=`FIO`>
      <ATTR TYPE=`SURNAME` VALUE=`HATTAB`/>
      <ATTR TYPE=`NAME` VALUE=`HASAN`/>
      <SOURCE> Hattab</SOURCE>
  </OBJECT>
  <OBJECT ID=`8` TYPE=`Organization`>
      <ARG CONST=`MINISTRY`/>
      <ARG CONST=`OF`/>
      <ARG CONST=`FOREIGN`/>
      <ARG CONST=`AFFAIR`/>
      <ARG CONST=`OF`/>
      <ARG CONST=`CHECHEN`/>
      <ARG CONST=`REPUBLIC`/>
      <SOURCE> Ministry of Foreign Affairs of the Chechen Republic</SOURCE>
  </OBJECT>
  <OBJECT ID=`9` TYPE=`Organization`>
      <ARG CONST=`CHECHEN`/>
      <ARG CONST=`SPECIAL`/>
      <ARG CONST=`MILITIA`/>
      <SOURCE> Chechen special militia</SOURCE>
  </OBJECT>
  <OBJECT ID=`10` TYPE=`Military_Force`>
      <ARG CONST=`JOINT`/>
      <ARG CONST=`FORCE`/>
      <ARG CONST=`OF`/>
      <ARG REF=`9`/>
      <SOURCE> Joint forces of Chechen special militia</SOURCE>
  </OBJECT>
  <OBJECT ID=`11` TYPE=`Military_Force`>
      <ARG CONST=`DIVISION`/>
      <ARG CONST=`OF`/>
      <ARG CONST=`FEDERAL`/>
      <ARG CONST=`FORCES`/>
      <SOURCE> Divisions of federal forces</SOURCE>
  </OBJECT>
  <OBJECT ID=`12` TYPE=`Place`>
      <ARG CONST=`SETTLEMENT`/>
      <ARG CONST=`STARYE`/>
      <ARG CONST=`ATAGI`/>
      <ARG CONST=`OF`/>
      <ARG CONST=`GROZNENSKY`/>
      <ARG CONST=`REGION`/>
      <SOURCE> settlement Starye Atagi of Groznensky region</SOURCE>
  </OBJECT>
  <OBJECT ID=`13` TYPE=`Weapon`>
      <ARG CONST=`SUB `/>
      <ARG CONST=`MACHINE`/>
      <ARG CONST=`GUN`/>
      <SOURCE> Sub machine guns</SOURCE>
  </OBJECT>
  <OBJECT ID=`14` TYPE=`Weapon`>
      <ARG CONST=`GRENADE`/>
      <SOURCE> Grenades</SOURCE>
  </OBJECT>
  <OBJECT ID=`15` TYPE=`Position`>
      <ARG CONST=`PARTICIPANT`/>
      <ARG CONST=`OF`/>
      <ARG CONST=`OPERATION`/>
      <SOURCE> Participants of the operation</SOURCE>
  </OBJECT>
  <RELATION TYPE=`SUCCESSOR`>
      <ARG REF=`6`/>
      <ARG REF=`7`/>
  </RELATION>
  <RELATION TYPE=`ASSISTANT`>
      <ARG REF=`3`/>
      <ARG REF=`6`/>
  <ACTION ID=`16` TYPE=`DESTROY`>
      <ARG CONST=`ARABIAN`/>
      <ARG REF=`3`/>
  </ACTION>
  <RELATION TYPE=`Where`>
      <ARG REF=`16`/>
      <ARG REF=`4`/>
  </RELATION>
  <ACTION ID=`17` TYPE=`INFORM`>
      <ARG REF=`8`/>
  </ACTION>
  <ACTION ID=`18` TYPE=`DESTROY`>
      <ARG REF=`10`/>
      <ARG REF=`11`/>
  </ACTION>
  <ACTION ID=`19` TYPE=`CHECK UP`>
      <ARG CONST=`ADDRESS`/>
  </ACTION>
  <ACTION ID=`20` TYPE=`FIND`>
      <ARG CONST=`1`/>
      <ARG CONST=`HOUSE`/>
      <ARG CONST=`HIDE`/>
      <ARG CONST=`PLACE`/>
      <ARG CONST=`CONFIDENTIAL`/>
      <ARG CONST=`ARABIAN`/>
      <ARG CONST=`DOCUMENT`/>
  </ACTION>
  <ACTION ID=`21` TYPE=`BE NO`>
      <ARG CONST=`LOSS`/>
      <ARG REF=`15`/>
  </ACTION>
  <SENTENCE>
      <ARG REF=`1`/>
      <ARG REF=`2`/>
      <ARG REF=`16`/>
<SOURCE>12:16 27.12.2002 One of leaders of insurgents - ArabianAbu-Tarik is destroyed in the Chechen Republic. </SOURCE>
  </SENTENCE>
  <SENTENCE>
      <ARG REF=`4`/>
      <ARG REF=`5`/>
      <ARG CONST=`MERCENARY`/>
      <ARG REF=`3`/>
      <ARG REF=`6`/>
      <ARG REF=`7`/>
      <ARG CONST=`DESTROY`/>
<SOURCE>In the Chechen Republic one of leaders of Islam terroristicgroup the mercenary Abu-Tarik - assistant of Abu al-Valod, successorof Hattab, is destroyed. </SOURCE>
  </SENTENCE>
  <SENTENCE>
      <ARG REF=`17`/>
      <ARG REF=`18`/>
      <ARG REF=`19`/>
<SOURCE>As have informed the Ministry of Foreign Affairs of theChechen Republic, joint forces of Chechen special militia anddivisions of federal forces destroy the insurgent in settlement StaryeAtagi of Groznensky region during the addressed check up. </SOURCE>
  </SENTENCE>
  <SENTENCE>
      <ARG REF=`20`/>
      <ARG CONST=`3`/>
      <ARG REF=`13`/>
      <ARG CONST=`AND`/>
      <ARG REF=`14`/>
      <ARG CONST=`AMMUNITION`/>
<SOURCE>In one of the houses there were found the hiding place withthe confidential Arabian documents, three sub-machine guns and grenades, ammunition. </SOURCE>
  </SENTENCE>
  <SENTENCE>
      <ARG REF=`21`/>
<SOURCE>There are no losses among the participants of theoperation</SOURCE>
  </SENTENCE>
</DOCUMENT>