An Algorithm for Open Text Semantic Parsing Page: 2
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ular instances such as the pizza I ordered. Other
semantic relations can also be identified, such as
coreference, complement, and others. Based on the
principle of compositionality, the sentence semantic
structure is recursive, similar to a tree.
The semantic parser analyzes shallow-level se-
mantics, which is derived directly from linguis-
tic knowledge, such as rules about semantic
role assignment, lexical semantic knowledge, and
syntactic-semantic mappings, without taking into
account any context or common sense knowledge.
The parser can be used as an intermediate semantic
processing tool before higher levels of text under-
3 Knowledge Bases for Semantic Parsing
One major problem faced by many natural language
understanding applications that rely on syntactic
analysis of text, is the fact that similar syntactic pat-
terns may introduce different semantic interpreta-
tions. Likewise, similar meanings can be syntac-
tically realized in many different ways. The seman-
tic parser attempts to solve this problem, and pro-
duces a syntax-independent representation of sen-
tence meaning, so that semantic constituents can be
accessed and processed in a more meaningful and
flexible way, avoiding the sometimes rigid interpre-
tations produced by a syntactic analyzer. For in-
stance, the sentences I boil water and water boils
contain a similar relation between water and boil,
even though they have different syntactic structures.
To deal with the large number of cases where the
same syntactic relation introduces different seman-
tic relations, we need knowledge about how to map
syntax to semantics. To this end, we use two main
types of knowledge - about words, and about rela-
tions between words. The first type of knowledge
is drawn from WordNet - a large lexical database
with rich information about words and concepts.
We refer to this as word-level knowledge. The lat-
ter is derived from FrameNet - a resource that con-
tains information about different situations, called
frames, in which semantic relations are syntacti-
cally realized in natural language sentences. We
call this sentence-level knowledge. In addition to
these two lexical knowledge bases, the parser also
utilizes a set of manually defined rules, which en-
code mappings from syntactic structures to seman-
tic relations, and which are also used to handle those
structures not explicitly addressed by FrameNet or
In this section, we describe the type of infor-
mation extracted from these knowledge bases, and
accessible to the semantic parser.
3.1 Frame Identification and Semantic Role
FrameNet (Johnson et al., 2002) provides the
knowledge needed to identify case frames and se-
mantic roles. FrameNet is based on the theory of
frame semantics, and defines a sentence level on-
tology. In frame semantics, a frame corresponds to
an interaction and its participants, both of which
denote a scenario, in which participants play some
kind of roles. A frame has a name, and we use this
name to identify the semantic relation that groups
together the semantic roles. In FrameNet, nouns,
verbs and adjectives can be used to identify frames.
Each annotated sentence in FrameNet exempli-
fies a possible syntactic realization for the seman-
tic roles associated with a frame for a given target
word. By extracting the syntactic features and cor-
responding semantic roles from all annotated sen-
tences in the FrameNet corpus, we are able to auto-
matically build a large set of rules that encode the
possible syntactic realizations of semantic frames.
In our implementation, we use only verbs as
target words for frame identification. Currently,
FrameNet defines about 1700 verbs attached to 230
different frames. To extend the parser coverage to
a larger subset of English verbs, we are using Verb-
Net (Kipper et al., 2000), which allows us to handle
a significantly larger set of English verbs.
VerbNet is a verb lexicon compatible with Word-
Net, but with explicitly stated syntactic and se-
mantic information using Levin's verb classification
(Levin, 1993). The fundamental assumption is that
the syntactic frames of a verb as an argument-taking
element are a direct reflection of the underlying se-
mantics. Therefore verbs in the same VerbNet class
usually share common FrameNet frames, and have
the same syntactic behavior. Hence, rules extracted
from FrameNet for a given verb can be easily ex-
tended to verbs in the same VerbNet class. To en-
sure a correct outcome, we have manually validated
the FrameNet-VerbNet mapping, and corrected the
few discrepancies that were observed between Verb-
Net classes and FrameNet frames.
3.1.1 Rules Learned from FrameNet
FrameNet data "is meant to be lexicographically rel-
evant, not statistically representative" (Johnson et
al., 2002), and therefore we are using FrameNet as
a starting point to derive rules for a rule-based se-
To build the rules, we are extracting several syn-
tactic features. Some are explicitly encoded in
show how this information is encoded in a format
FrameNet, such as the grammatical function (GF)
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Shi, Lei & Mihalcea, Rada, 1974-. An Algorithm for Open Text Semantic Parsing, paper, August 2004; (digital.library.unt.edu/ark:/67531/metadc30953/m1/2/: accessed July 25, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.