An Algorithm for Open Text Semantic Parsing Page: 1
The following text was automatically extracted from the image on this page using optical character recognition software:
An Algorithm for Open Text Semantic Parsing
Lei Shi and Rada Mihalcea
Department of Computer Science
University of North Texas
This paper describes an algorithm for open text shal-
low semantic parsing. The algorithm relies on a
frame dataset (FrameNet) and a semantic network
(WordNet), to identify semantic relations between
words in open text, as well as shallow semantic fea-
tures associated with concepts in the text. Parsing
semantic structures allows semantic units and con-
stituents to be accessed and processed in a more
meaningful way than syntactic parsing, moving the
automation of understanding natural language text
to a higher level.
The goal of the semantic parser is to analyze the
semantic structure of a natural language sentence.
Similar in spirit with the syntactic parser - whose
goal is to parse a valid natural language sentence
into a parse tree indicating how the sentence can
be syntactically decomposed into smaller syntactic
constituents - the purpose of the semantic parser is
to analyze the structure of sentence meaning. Sen-
tence meaning is composed by entities and interac-
tions between entities, where entities are assigned
semantic roles, and can be further modified by other
modifiers. The meaning of a sentence is decom-
posed into smaller semantic units connected by var-
ious semantic relations by the principle of compo-
sitionality, and the parser represents the semantic
structure - including semantic units as well as se-
mantic relations, connecting them into a formal for-
In this paper, we describe the main components
of the semantic parser, and illustrate the basic pro-
cedures involved in parsing semantically open text.
We believe that such structures, reflecting various
levels of semantic interpretation of the text, can be
used to improve the quality of text processing appli-
cations, by taking into account the meaning of text.
The paper is organized as follows. We first de-
scribe the semantic structure of English sentences,
as the basis for semantic parsing. We then intro-
duce the knowledge bases utilized by the parser, and
show how we use this knowledge in the process of
semantic parsing. Next, we describe the parsing
algorithm and elaborate on each of the three main
steps involved in the process of semantic parsing:
(1) syntactic and shallow semantic analysis, (2) se-
mantic role assignment, and (3) application of de-
fault rules. Finally, we illustrate the parsing process
with several examples, and show how the semantic
parsing algorithm can be integrated into other lan-
guage processing systems.
2 Semantic Structure
Semantics is the denotation of a string of symbols,
either a sentence or a word. Similar to a syn-
tactic parser, which shows how a larger string is
formed by smaller strings from a formal point of
view, the semantic parser shows how the denotation
of a larger string - sentence, is formed by deno-
tations of smaller strings - words. Syntactic rela-
tions can be described using a set of rules about how
a sentence string is formally generated using word
strings. Instead, semantic relations between seman-
tic constituents depend on our understanding of the
world, which is across languages and syntax.
We can model the sentence semantics as describ-
ing entities and interactions between entities. Enti-
ties can represent physical objects, as well as time,
places, or ideas, and are usually formally realized
as nouns or noun phrases. Interactions, usually real-
ized as verbs, describe relationships or interactions
between participating entities. Note that a partic-
ipant can also be an interaction, which can be re-
garded as an entity nominalized from an interaction.
We assign semantic roles to participants, and their
semantic relations are identified by the case frame
introduced by their interaction. In a sentence, par-
ticipants and interactions can be further modified
by various modifiers, including descriptive modi-
fiers that describe attributes such as drive slowly,
restrictive modifiers that enforce a general denota-
tion to become more specific such as musical in-
strument, referential modifiers that indicate partic-
Here’s what’s next.
This paper can be searched. Note: Results may vary based on the legibility of text within the document.
Tools / Downloads
Get a copy of this page or view the extracted text.
Citing and Sharing
Basic information for referencing this web page. We also provide extended guidance on usage rights, references, copying or embedding.
Reference the current page of this Paper.
Shi, Lei & Mihalcea, Rada, 1974-. An Algorithm for Open Text Semantic Parsing, paper, August 2004; (digital.library.unt.edu/ark:/67531/metadc30953/m1/1/: accessed December 18, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.