2,182 research outputs found
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
Event-based Access to Historical Italian War Memoirs
The progressive digitization of historical archives provides new, often
domain specific, textual resources that report on facts and events which have
happened in the past; among these, memoirs are a very common type of primary
source. In this paper, we present an approach for extracting information from
Italian historical war memoirs and turning it into structured knowledge. This
is based on the semantic notions of events, participants and roles. We evaluate
quantitatively each of the key-steps of our approach and provide a graph-based
representation of the extracted knowledge, which allows to move between a Close
and a Distant Reading of the collection.Comment: 23 pages, 6 figure
Discovery of Linguistic Relations Using Lexical Attraction
This work has been motivated by two long term goals: to understand how humans
learn language and to build programs that can understand language. Using a
representation that makes the relevant features explicit is a prerequisite for
successful learning and understanding. Therefore, I chose to represent
relations between individual words explicitly in my model. Lexical attraction
is defined as the likelihood of such relations. I introduce a new class of
probabilistic language models named lexical attraction models which can
represent long distance relations between words and I formalize this new class
of models using information theory.
Within the framework of lexical attraction, I developed an unsupervised
language acquisition program that learns to identify linguistic relations in a
given sentence. The only explicitly represented linguistic knowledge in the
program is lexical attraction. There is no initial grammar or lexicon built in
and the only input is raw text. Learning and processing are interdigitated. The
processor uses the regularities detected by the learner to impose structure on
the input. This structure enables the learner to detect higher level
regularities. Using this bootstrapping procedure, the program was trained on
100 million words of Associated Press material and was able to achieve 60%
precision and 50% recall in finding relations between content-words. Using
knowledge of lexical attraction, the program can identify the correct relations
in syntactically ambiguous sentences such as ``I saw the Statue of Liberty
flying over New York.''Comment: dissertation, 56 page
Distributional Measures of Semantic Distance: A Survey
The ability to mimic human notions of semantic distance has widespread
applications. Some measures rely only on raw text (distributional measures) and
some rely on knowledge sources such as WordNet. Although extensive studies have
been performed to compare WordNet-based measures with human judgment, the use
of distributional measures as proxies to estimate semantic distance has
received little attention. Even though they have traditionally performed poorly
when compared to WordNet-based measures, they lay claim to certain uniquely
attractive features, such as their applicability in resource-poor languages and
their ability to mimic both semantic similarity and semantic relatedness.
Therefore, this paper presents a detailed study of distributional measures.
Particular attention is paid to flesh out the strengths and limitations of both
WordNet-based and distributional measures, and how distributional measures of
distance can be brought more in line with human notions of semantic distance.
We conclude with a brief discussion of recent work on hybrid measures
Ontology-Aware Token Embeddings for Prepositional Phrase Attachment
Type-level word embeddings use the same set of parameters to represent all
instances of a word regardless of its context, ignoring the inherent lexical
ambiguity in language. Instead, we embed semantic concepts (or synsets) as
defined in WordNet and represent a word token in a particular context by
estimating a distribution over relevant semantic concepts. We use the new,
context-sensitive embeddings in a model for predicting prepositional phrase(PP)
attachments and jointly learn the concept embeddings and model parameters. We
show that using context-sensitive embeddings improves the accuracy of the PP
attachment model by 5.4% absolute points, which amounts to a 34.4% relative
reduction in errors.Comment: ACL 201
On the Effect of Semantically Enriched Context Models on Software Modularization
Many of the existing approaches for program comprehension rely on the
linguistic information found in source code, such as identifier names and
comments. Semantic clustering is one such technique for modularization of the
system that relies on the informal semantics of the program, encoded in the
vocabulary used in the source code. Treating the source code as a collection of
tokens loses the semantic information embedded within the identifiers. We try
to overcome this problem by introducing context models for source code
identifiers to obtain a semantic kernel, which can be used for both deriving
the topics that run through the system as well as their clustering. In the
first model, we abstract an identifier to its type representation and build on
this notion of context to construct contextual vector representation of the
source code. The second notion of context is defined based on the flow of data
between identifiers to represent a module as a dependency graph where the nodes
correspond to identifiers and the edges represent the data dependencies between
pairs of identifiers. We have applied our approach to 10 medium-sized open
source Java projects, and show that by introducing contexts for identifiers,
the quality of the modularization of the software systems is improved. Both of
the context models give results that are superior to the plain vector
representation of documents. In some cases, the authoritativeness of
decompositions is improved by 67%. Furthermore, a more detailed evaluation of
our approach on JEdit, an open source editor, demonstrates that inferred topics
through performing topic analysis on the contextual representations are more
meaningful compared to the plain representation of the documents. The proposed
approach in introducing a context model for source code identifiers paves the
way for building tools that support developers in program comprehension tasks
such as application and domain concept location, software modularization and
topic analysis
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