Discourse Representation Structure
A Discourse Representation Structure is semantic representation of a discourse as it unfolds over time.
- AKA: DRS.
- Context:
- It can be converted to a graph representation.
- It requires a set of
- Example(s):
- The sentence "A farmer chased and caught a donkey." can be represented by the DRS set:
[x, y,: farmer(x), donkey(y), chased(x,y), caught(x,y)]wherexandyare 2 discourse referents andfarmer(x),donkey(y),chased(x,y),caught(x,y)are 4 DRS conditions. - …
- The sentence "A farmer chased and caught a donkey." can be represented by the DRS set:
- Counter-Example(s):
- See: Discourse Representation Theory, Natural Language Processing.
References
2017
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Discourse_representation_theory#Discourse_representation_structures Retrieved:2017-6-18.
- DRT uses discourse representation structures (DRS) to represent a hearer's mental representation of a discourse as it unfolds over time. There are two critical components to a DRS:
- A set of discourse referents representing entities which are under discussion.
- A set of DRS conditions representing information that has been given about discourse referents.
- Consider Sentence (1) below:
(1) A farmer owns a donkey.
The DRS of (1) can be notated as (2) below:
(2) [x,y: farmer(x), donkey(y), owns(x,y)]
What (2) says is that there are two discourse referents, x and y, and three discourse conditions farmer, donkey, and owns, such that the condition farmer holds of x, donkey holds of y, and owns holds of the pair x and y.
Informally, the DRS in (2) is true in a given model of evaluation if and only if there are entities in that model which satisfy the conditions. So, if a model contains two individuals, and one is a farmer, the other is a donkey, and the first owns the second, the DRS in (2) is true in that model.
Uttering subsequent sentences result in the existing DRS being updated.
(3) He beats it.
Uttering (3) after (1) results in the DRS in (2) being updated as follows, in (4) (assuming a way to disambiguate which pronoun refers to which individual).
(4) [x,y: farmer(x), donkey(y), own(x,y), beat(x,y)]
Successive utterances of sentences work in a similar way, although the process is somewhat more complicated for more complex sentences such as sentences containing negation, and conditionals.
- DRT uses discourse representation structures (DRS) to represent a hearer's mental representation of a discourse as it unfolds over time. There are two critical components to a DRS:
2016A
- (Guerts et at., 2016) ⇒ Geurts, Bart, Beaver, David I. and Maier, Emar, "Discourse Representation Theory", The Stanford Encyclopedia of Philosophy (Spring 2016 Edition), Edward N. Zalta (ed.) URL https://plato.stanford.edu/entries/discourse-representation-theory/#BasDRT
- Setion 3.1 - The key ideas: A discourse representation structure (DRS) is a mental representation built up by the hearer as the discourse unfolds. A DRS consists of two parts: a universe of so-called “discourse referents”, which represent the objects under discussion, and a set of DRS-conditions which encode the information that has accumulated on these discourse referents. The following DRS represents the information that there are two individuals, one of which is a farmer, the other a donkey, and that the former chased the latter:
[math]\displaystyle{ [1]\quad\quad }[/math] style="| [x, y: farmer(x), donkey(y), chased(x,y)]
The universe of this DRS contains two discourse referents, x and y, and its condition set is {farmer(x), donkey(y), chased(x,y)}.
A DRS like the one in [1] can be given a straightforward model-theoretic interpretation. In DRT this is done by means of embedding functions, which are partial functions from discourse referents to individuals in a given model M; or put otherwise, an embedding function is a partial variable assignment. An embedding function f verifies [1] in M if the domain of f includes at least x and y, and according to M it is the case that f(x) is a farmer, f(y) is a donkey, and f(x) chased f(y).
Meanwhile it will have become clear that the DRS in [1] is designed to reflect the intuitive meaning of:
[math]\displaystyle{ [2]\quad\quad }[/math] A farmer chased a donkey.
- Setion 3.1 - The key ideas: A discourse representation structure (DRS) is a mental representation built up by the hearer as the discourse unfolds. A DRS consists of two parts: a universe of so-called “discourse referents”, which represent the objects under discussion, and a set of DRS-conditions which encode the information that has accumulated on these discourse referents. The following DRS represents the information that there are two individuals, one of which is a farmer, the other a donkey, and that the former chased the latter:
2016B
- (Dakpta & Kubler, 2016) ⇒ Dakota, D., & Kübler, S. (2016, September). From Discourse Representation Structure to event semantics: A simple conversion?. In Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on (pp. 343-352). [DOI:10.15439/2016F440] , (free online pdf)
- Abstract: Many applications in Natural Language Processing require a semantic analysis of sentences in terms of truth-conditional representations, often with specific desiderata in terms of which information needs to be included in the semantic analysis. However, there are only very few tools that allow such an analysis. We investigate the representations of an automatic analysis pipeline of the C&C parser and Boxer to determine whether Boxer's analyses in form of Discourse Representation Structure can be successfully converted into a more surface oriented event semantic representation, which will serve as input for a fusion algorithm for fusing hard and soft information. We use a data set of synthetic counter intelligence messages for our investigation. We provide a basic pipeline for conversion and subsequently discuss areas in which ambiguities and differences between the semantic representations present challenges in the conversion process.
2008
- (Arakawa, 2008) ⇒ Naoya Arakawa. (2008). “Semantic Analysis based on Ontologies with Semantic Web Standards.” In: Computer-aided Acquisition of Semantic Knowledge (CASK 2008).
- Section 2, Semantic Representation in Graphs: Our semantic representation draws most upon DRT (Discourse Representation Theory) [2] and SDRT [3] (“S” stands for “Segmented”). (S)DRT uses semantic representation called DRS (Discourse Representation Structure), which is a nested structure in which logical terms and predicates are placed. SDRT is a theory in dynamic semantics and purports to explain various discourse phenomena as well as semantics of simple sentences (and the versatility is the reason for our adoption). Another feature of our semantic representation is that it has a graph representation. The reason for this is that we want our system to work with the Semantic Web, whose data representation model is in the graph form (i.e., RDF). While a DRS is not normally considered as a graph, there are theories that use semantic representations in graph forms. One example is Conceptual Graphs proposed by Sowa [4]. Conceptual Graphs represent a set of predicate logic formulas as a graph. Another example is CDL (Conceptual Description Language) proposed by Yokoi et al [5]. To be more specific, both Conceptual Graphs and CDL are hyper-graphs, i.e., nested graphs having sub-graphs as their nodes. Such nested structure or hyper-graphs are required, for example, for representing nested sentences.
Now, as a DRS is nested structure containing predicate logic formulas, it can be converted into graphs in a fairly straightforward way. To represent predicate logic formulas in graphs, we can draw upon Conceptual Graphs. The nested structure of a DRS can be represented as a hypergraph.
- Section 2, Semantic Representation in Graphs: Our semantic representation draws most upon DRT (Discourse Representation Theory) [2] and SDRT [3] (“S” stands for “Segmented”). (S)DRT uses semantic representation called DRS (Discourse Representation Structure), which is a nested structure in which logical terms and predicates are placed. SDRT is a theory in dynamic semantics and purports to explain various discourse phenomena as well as semantics of simple sentences (and the versatility is the reason for our adoption). Another feature of our semantic representation is that it has a graph representation. The reason for this is that we want our system to work with the Semantic Web, whose data representation model is in the graph form (i.e., RDF). While a DRS is not normally considered as a graph, there are theories that use semantic representations in graph forms. One example is Conceptual Graphs proposed by Sowa [4]. Conceptual Graphs represent a set of predicate logic formulas as a graph. Another example is CDL (Conceptual Description Language) proposed by Yokoi et al [5]. To be more specific, both Conceptual Graphs and CDL are hyper-graphs, i.e., nested graphs having sub-graphs as their nodes. Such nested structure or hyper-graphs are required, for example, for representing nested sentences.