In this article will discuss current approaches to the representation of linguistic information. Particular attention is paid Minimal Recursion Semantic (MRS) and the Robust Minimal Recursion Semantic (RMRS) as one of the most promising directions. Shows the use of Minimal Recursion Semantic (MRS) for the Russian languag

Kanishcheva, O. V.

Канищева, Ольга Валерьевна

Library of National Technical University "Kharkiv Polytechnic Institute"

The 1th International Conference «Advanced Information Systems and Technologies, AIST 2012» 
15-18 May 2012, Sumy, Ukraine 
Using of Minimal Recursion Semantic representation 
for linguistic information 
Kanishcheva O. V. 
National Technical University “Kharkov Polytechnic Institute”, olya-kanisheva@rambler.ru 
 
In this article will discuss current approaches to the 
representation of linguistic information. Particular 
attention is paid Minimal Recursion Semantic (MRS) 
and the Robust Minimal Recursion Semantic (RMRS) 
as one of the most promising directions. Shows the use 
of Minimal Recursion Semantic (MRS) for the Russian 
language. 
 
INTRODUCTION 
The choice of a formal language to represent 
the linguistic knowledge is quite a challenge, 
since this formalism to describe not only natural 
language, but it should be fairly easy to 
implement. The development of natural-
language systems, one way or another, faced 
with the problem of representation, storage, and 
interpretation of linguistic information. In this 
case, the representation of the data includes the 
tools and the formalisms used for presentation, 
methods of storage in the processing and 
interpretation of the data system [1]. 
There are different classification approaches 
to the representation of linguistic data. In this 
paper we shall use the following classification: 
1) Approaches based on the markup 
(markup-based), in which additional information 
is stored directly in the text in the form of 
additional markup (HTML, SGML or XML). 
2) Approaches based on the annotations 
(annotation-based), in which information is 
stored separately and contains references to the 
source code. 
3) Approaches based on abstractions 
(abstraction-based), in which the text is stored 
only as part of a data structure, which in turn 
represents all information in the form based on a 
specific formalism. 
4) Approaches, in which there are no 
restrictions on the representation of the data. 
In this paper we consider a more detailed 
approach based on abstractions, because they are 
based on evidence establishing the structure, 
which is a common means to represent the 
linguistic information. Many formalisms for 
analysis using them. 
Of particular interest are formalisms such as 
Minimal Recursion Semantic (MRS) and the 
Robust Minimal Recursion Semantic (RMRS). 
The main idea of this formalism is to convert a 
nested structure in the flat. Thus, the nested 
structure of the attributes (or predicates) can be 
transformed in a variety of structures (which can 
be combined symbols of conjunction). The 
formalism is an extension of RMRS MRS, 
which is the main difference lies in the fact that 
the structures of several signs are divided into 
binary predicates. 
 
MINIMAL RECURSION SEMANTIC 
Minimal Recursion Semantic [3], this 
formalism is used for semantic representation of 
data by means of elementary predicates. It is 
widely used, especially for linguistic theories 
(HPSG). Robust Minimal Recursion Semantic 
[4] is a variant of MRS. While the MRS, in 
foreign practice were used for manual 
processing grammar HPSG, RMRS is also 
suitable for use in surface analysis methods of 
textual information, including the fragmentation 
of phrases and stochastic analyzers that operate 
without a detailed glossary. 
MRS – semantic representation, which uses 
first-order predicate logic. This is not a semantic 
theory based on logical formulas. MRS reduces 
the computational complexity for the 
construction of linguistic structures that 
preserves value for the target language. 
Almost MRS was performed in English 
Resource Grammar, broadcoverage HPSG 
grammar using MRS as its semantic 
representation. Another application of this 
formalism can be found in machine translation, 
statistical analysis, dialog systems, information 
retrieval, ontologies [2]. 
 
THE PURPOSE OF THIS WORK 
The purpose of this study is to examine this 
formalism to represent data as Minimal 
Recursion Semantic. Show it to use the Russian 
language. 
USING MINIMAL RECURSION SEMANTIC FOR 
SUBMISSION DATA IN RUSSIAN 
An MRS representation consists of a triple, 
as shown in (1). This section explains all three 
elements and their purposes. There are, 
furthermore, two important notations of how to 
present MRSs, the standard way and as MRS 
graphs, which are both introduced below. 
  < hook , EP bag , handle constraints >  (1) 
The first element is the hook of the structure. 
It is important during the semantic of 
composition of complete MRSs. The second 
element is the EP bag. It is a set of predicates 
that describes the lexical and some relational 
semantic information contained in a sentence. 
The last element is a set of handle constraints 
that specify certain scopal relations of the 
elements in the EP bag. 
At the heart of an MRS representation is a set 
of elementary predications (EP) called the EP 
bag. EPs are basic relations, similar to predicates 
in first-order logic. They normally correspond to 
a single lexeme, often referred to by its lemma. 
Every EP is marked by a label, has a relation 
name and a certain number of arguments, 
depending on the arity of the predicate. (2) 
shows the general notation of an EP. 
  label: relation(argument0, ..., argumentn) (2) 
(3) presents an EP bag for the example sentence 
Каждый человек вероятно любит природу. 
EP bag: 
l1: каждый q (x1; h1; h2, h3), 
l3: человек n (x1), 
l4: вероятно adv (e1; h4), 
(3) 
l5: любит v (e2; x1; x2), 
l6: природу n (x2). 
Handle constraints: h1 =q l3, h3 =q l5. 
Relations that describe lexical words start with 
an underscore, followed by the lemma of the 
word, followed by another underscore and the 
part-of-speech information. Optionally, a last 
underscore can separate the part-of-speech from 
a number that constitutes an additional sense 
distinction among words with the same lemma 
and part-of-speech. 
The logical conjunction operator ^ is given a 
special status in the MRS formalism [5, 6]. In 
natural language it is generally used for 
composing semantic expressions, while the 
other logical connectives (disjunction _, etc.) 
only contribute to the semantics when they are 
lexically licensed. Also, they appear in more 
restricted contexts. As a consequence, EP 
conjunctions are made implicit by using 
identical labels for all members of the 
conjunction. Our phrase in (3) is constructed 
using identical labels, but note that implicit 
conjunctions are versatile in their potential 
usage. 
Prepositional phrases, for example, are 
constructed in the same way, labeling the 
preposition EP with the same label as the EP it 
is attached to. 
There are different types of variables that are 
used in MRS [7]. Table 1 lists all of them. 
Variables can have features attached to them 
that can carry morphological information. For 
example, nominal variables can have values for 
person, number and gender, while event 
variables carry tense and mood. 
Table 1. Different types of variables used in the 
context of MRS 
Variable Usage 
a anchors uniquely identify an EP (only in 
RMRS) 
l labels "tag" one or more EPs 
h holes are arguments slots for embedding 
other EPs 
x nominal variables are introduced by 
nouns and adjectives 
e event variables are introduced by verbal 
and adverbial EPs 
u used to mark unspecified obligatory 
arguments 
i used to mark unspecified optional 
arguments 
Every EP has characteristic arguments that 
get introduced depending on the part-of-speech. 
For nouns and adjective, the first argument is 
always a nominal variable that stands for the 
nominal object. 
Holes can be seen as empty slots for other 
EPs. By equating the holes with EP labels, a 
predicate logic formula with embedded 
predicates can be created. Such linkings are 
referred to as configurations or scope-resolved 
MRSs that represent the individual linguistic 
readings for a sentence described by an MRS. 
Possible configurations for the predicates in (3) 
are shown in Figure 1. The MRS itself, 
however, is a flat representation and avoids 
embedding. Moreover, it is possible 
configurations that can be constructed by 
equating holes and labels underspecified 
concerning the scope relations and stands for the 
set of all. 
 
Figure 1 – Configuration for the EPs 
 
CONCLUSIONS 
This paper demonstrates the use of Minimal 
Recursion Semantic (MRS) for the Russian 
language. This study is promising, since the use 
of multilingual language resources structures 
MRS, makes the data more useful for further 
deep and surface processing. In the future, plans 
to use the formal-semantic representation to 
generate new knowledge, with the assistance of 
the algebra of predicates and predicate 
operations [8, 9]. 
RMRSs have already been used in systems 
for question answering, information extraction, 
email response, creative authoring and ontology 
extraction. 
 
REFERENCES 
[1] Автоматическая обработка текстов на 
естественном языке и компьютерная лингвистика 
: учеб. пособие / Большакова Е.И., Клышинский 
Э.С., Ландэ Д.В., Носков А.А., Пескова О.В., 
Ягунова Е.В. – М.: МИЭМ, 2011. – 272 с.  
[2] Azarova I., Sinopalnikova A. Adjectives in Russnet 
// International WordNet Conference, GWC 2004 
Brno, Czech Republic, January 20-23, 2004. P. 251-
259. 
[3] Copestake, Ann, Dan Flickinger, Carl Pollard and 
Ivan A. Sag. Minimal Recursion Semantics: an 
Introduction. In preparation, 1999. 
[4] Ann Copestake. Applying Robust Semantics. In 
Proceedings of the 10th Conference of the Pacfic 
Assocation for Computational Linguistics 
(PACLING), pages 1-12, Melbourne, Australia, 
2007. 
[5] Copestake, Ann, Dan Flickinger, Ivan A. Sag and 
Carl Pollard (2005) „Minimal Recursion Semantics: 
An introduction‟, Research on Language and 
Computation, 3(2–3), 281-332. 
[6] Copestake, Ann, Dan Flickinger, Ivan A. Sag and 
Carl Pollard (2005) „Minimal Recursion Semantics: 
An introduction‟, Research on Language and 
Computation, 3(2–3), 281–332. 
[7] Copestake, Ann and Dan Flickinger (2000) „An 
open-source grammar development environment and 
broad-coverage English grammar using HPSG‟, 
Proceedings of the Second conference on Language 
Resources and Evaluation (LREC-2000), Athens, 
Greece, pp. 591–600. 
[8] Partee, Barbara H. and Vladimir Borschev (1998). 
Integrating lexical and formal semantics: Genitives, 
relational nouns, and type-shifting. In Proceedings of 
the Second Tbilisi Symposium on Language, Logic, 
and Computation, ed. Robin Cooper and Thomas 
Gamkrelidze, 229-241. Tbilisi: Center on Language, 
Logic, Speech, Tbilisi State University. 
[9] Kanishcheva О. V. Using the algebra predicate and 
predicate operations for the description of natural – 
language relations in tasks for representations 
knowledge / О. V. Kanishcheva // XII International 
PhD Workshop OWD 2010, 23-26 October 2010. –
  Gliwice and Wisla, 2010. – P. 67-69. 
 
 


Using of Minimal Recursion Semantic representation for linguistic information

Electronic National Technical University &quot;Kharkiv Polytechnic Institute&quot; Institutional Repository (eNTUKhPIIR)

The 1th International Conference «Advanced Information Systems and Technologies, AIST 2012» 15-18 May 2012, Sumy, Ukraine Using of Minimal Recursion Semantic representation for linguistic information Kanishcheva O. V. National Technical University “Kharkov Polytechnic Institute”, olya-kanisheva@rambler.ru  In this article will discuss current approaches to the representation of linguistic information. Particular attention is paid Minimal Recursion Semantic (MRS) and the Robust Minimal Recursion Semantic (RMRS) as one of the most promising directions. Shows the use of Minimal Recursion Semantic (MRS) for the Russian language.  INTRODUCTION The choice of a formal language to represent the linguistic knowledge is quite a challenge, since this formalism to describe not only natural language, but it should be fairly easy to implement. The development of natural-language systems, one way or another, faced with the problem of representation, storage, and interpretation of linguistic information. In this case, the representation of the data includes the tools and the formalisms used for presentation, methods of storage in the processing and interpretation of the data system [1]. There are different classification approaches to the representation of linguistic data. In this paper we shall use the following classification: 1) Approaches based on the markup (markup-based), in which additional information is stored directly in the text in the form of additional markup (HTML, SGML or XML). 2) Approaches based on the annotations (annotation-based), in which information is stored separately and contains references to the source code. 3) Approaches based on abstractions (abstraction-based), in which the text is stored only as part of a data structure, which in turn represents all information in the form based on a specific formalism. 4) Approaches, in which there are no restrictions on the representation of the data. In this paper we consider a more detailed approach based on abstractions, because they are based on evidence establishing the structure, which is a common means to represent the linguistic information. Many formalisms for analysis using them. Of particular interest are formalisms such as Minimal Recursion Semantic (MRS) and the Robust Minimal Recursion Semantic (RMRS). The main idea of this formalism is to convert a nested structure in the flat. Thus, the nested structure of the attributes (or predicates) can be transformed in a variety of structures (which can be combined symbols of conjunction). The formalism is an extension of RMRS MRS, which is the main difference lies in the fact that the structures of several signs are divided into binary predicates.  MINIMAL RECURSION SEMANTIC Minimal Recursion Semantic [3], this formalism is used for semantic representation of data by means of elementary predicates. It is widely used, especially for linguistic theories (HPSG). Robust Minimal Recursion Semantic [4] is a variant of MRS. While the MRS, in foreign practice were used for manual processing grammar HPSG, RMRS is also suitable for use in surface analysis methods of textual information, including the fragmentation of phrases and stochastic analyzers that operate without a detailed glossary. MRS – semantic representation, which uses first-order predicate logic. This is not a semantic theory based on logical formulas. MRS reduces the computational complexity for the construction of linguistic structures that preserves value for the target language. Almost MRS was performed in English Resource Grammar, broadcoverage HPSG grammar using MRS as its semantic representation. Another application of this formalism can be found in machine translation, statistical analysis, dialog systems, information retrieval, ontologies [2].  THE PURPOSE OF THIS WORK The purpose of this study is to examine this formalism to represent data as Minimal Recursion Semantic. Show it to use the Russian language. USING MINIMAL RECURSION SEMANTIC FOR SUBMISSION DATA IN RUSSIAN An MRS representation consists of a triple, as shown in (1). This section explains all three elements and their purposes. There are, furthermore, two important notations of how to present MRSs, the standard way and as MRS graphs, which are both introduced below.   < hook , EP bag , handle constraints >  (1) The first element is the hook of the structure. It is important during the semantic of composition of complete MRSs. The second element is the EP bag. It is a set of predicates that describes the lexical and some relational semantic information contained in a sentence. The last element is a set of handle constraints that specify certain scopal relations of the elements in the EP bag. At the heart of an MRS representation is a set of elementary predications (EP) called the EP bag. EPs are basic relations, similar to predicates in first-order logic. They normally correspond to a single lexeme, often referred to by its lemma. Every EP is marked by a label, has a relation name and a certain number of arguments, depending on the arity of the predicate. (2) shows the general notation of an EP.   label: relation(argument0, ..., argumentn) (2) (3) presents an EP bag for the example sentence Каждый человек вероятно любит природу. EP bag: l1: каждый q (x1; h1; h2, h3), l3: человек n (x1), l4: вероятно adv (e1; h4), (3) l5: любит v (e2; x1; x2), l6: природу n (x2). Handle constraints: h1 =q l3, h3 =q l5. Relations that describe lexical words start with an underscore, followed by the lemma of the word, followed by another underscore and the part-of-speech information. Optionally, a last underscore can separate the part-of-speech from a number that constitutes an additional sense distinction among words with the same lemma and part-of-speech. The logical conjunction operator ^ is given a special status in the MRS formalism [5, 6]. In natural language it is generally used for composing semantic expressions, while the other logical connectives (disjunction _, etc.) only contribute to the semantics when they are lexically licensed. Also, they appear in more restricted contexts. As a consequence, EP conjunctions are made implicit by using identical labels for all members of the conjunction. Our phrase in (3) is constructed using identical labels, but note that implicit conjunctions are versatile in their potential usage. Prepositional phrases, for example, are constructed in the same way, labeling the preposition EP with the same label as the EP it is attached to. There are different types of variables that are used in MRS [7]. Table 1 lists all of them. Variables can have features attached to them that can carry morphological information. For example, nominal variables can have values for person, number and gender, while event variables carry tense and mood. Table 1. Different types of variables used in the context of MRS Variable Usage a anchors uniquely identify an EP (only in RMRS) l labels "tag" one or more EPs h holes are arguments slots for embedding other EPs x nominal variables are introduced by nouns and adjectives e event variables are introduced by verbal and adverbial EPs u used to mark unspecified obligatory arguments i used to mark unspecified optional arguments Every EP has characteristic arguments that get introduced depending on the part-of-speech. For nouns and adjective, the first argument is always a nominal variable that stands for the nominal object. Holes can be seen as empty slots for other EPs. By equating the holes with EP labels, a predicate logic formula with embedded predicates can be created. Such linkings are referred to as configurations or scope-resolved MRSs that represent the individual linguistic readings for a sentence described by an MRS. Possible configurations for the predicates in (3) are shown in Figure 1. The MRS itself, however, is a flat representation and avoids embedding. Moreover, it is possible configurations that can be constructed by equating holes and labels underspecified concerning the scope relations and stands for the set of all.  Figure 1 – Configuration for the EPs  CONCLUSIONS This paper demonstrates the use of Minimal Recursion Semantic (MRS) for the Russian language. This study is promising, since the use of multilingual language resources structures MRS, makes the data more useful for further deep and surface processing. In the future, plans to use the formal-semantic representation to generate new knowledge, with the assistance of the algebra of predicates and predicate operations [8, 9]. RMRSs have already been used in systems for question answering, information extraction, email response, creative authoring and ontology extraction.  REFERENCES [1] Автоматическая обработка текстов на естественном языке и компьютерная лингвистика : учеб. пособие / Большакова Е.И., Клышинский Э.С., Ландэ Д.В., Носков А.А., Пескова О.В., Ягунова Е.В. – М.: МИЭМ, 2011. – 272 с.  [2] Azarova I., Sinopalnikova A. Adjectives in Russnet // International WordNet Conference, GWC 2004 Brno, Czech Republic, January 20-23, 2004. P. 251-259. [3] Copestake, Ann, Dan Flickinger, Carl Pollard and Ivan A. Sag. Minimal Recursion Semantics: an Introduction. In preparation, 1999. [4] Ann Copestake. Applying Robust Semantics. In Proceedings of the 10th Conference of the Pacfic Assocation for Computational Linguistics (PACLING), pages 1-12, Melbourne, Australia, 2007. [5] Copestake, Ann, Dan Flickinger, Ivan A. Sag and Carl Pollard (2005) „Minimal Recursion Semantics: An introduction‟, Research on Language and Computation, 3(2–3), 281-332. [6] Copestake, Ann, Dan Flickinger, Ivan A. Sag and Carl Pollard (2005) „Minimal Recursion Semantics: An introduction‟, Research on Language and Computation, 3(2–3), 281–332. [7] Copestake, Ann and Dan Flickinger (2000) „An open-source grammar development environment and broad-coverage English grammar using HPSG‟, Proceedings of the Second conference on Language Resources and Evaluation (LREC-2000), Athens, Greece, pp. 591–600. [8] Partee, Barbara H. and Vladimir Borschev (1998). Integrating lexical and formal semantics: Genitives, relational nouns, and type-shifting. In Proceedings of the Second Tbilisi Symposium on Language, Logic, and Computation, ed. Robin Cooper and Thomas Gamkrelidze, 229-241. Tbilisi: Center on Language, Logic, Speech, Tbilisi State University. [9] Kanishcheva О. V. Using the algebra predicate and predicate operations for the description of natural – language relations in tasks for representations knowledge / О. V. Kanishcheva // XII International PhD Workshop OWD 2010, 23-26 October 2010. –  Gliwice and Wisla, 2010. – P. 67-69.   

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Using of Minimal Recursion Semantic representation for linguistic information

Abstract

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Library of National Technical University "Kharkiv Polytechnic Institute"

Electronic National Technical University "Kharkiv Polytechnic Institute" Institutional Repository (eNTUKhPIIR)

Using of Minimal Recursion Semantic representation for linguistic information

Abstract

Similar works

Full text

Available Versions

Library of National Technical University "Kharkiv Polytechnic Institute"

Electronic National Technical University &quot;Kharkiv Polytechnic Institute&quot; Institutional Repository (eNTUKhPIIR)

Electronic National Technical University "Kharkiv Polytechnic Institute" Institutional Repository (eNTUKhPIIR)