14,063 research outputs found
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
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
Modeling of Phenomena and Dynamic Logic of Phenomena
Modeling of complex phenomena such as the mind presents tremendous
computational complexity challenges. Modeling field theory (MFT) addresses
these challenges in a non-traditional way. The main idea behind MFT is to match
levels of uncertainty of the model (also, problem or theory) with levels of
uncertainty of the evaluation criterion used to identify that model. When a
model becomes more certain, then the evaluation criterion is adjusted
dynamically to match that change to the model. This process is called the
Dynamic Logic of Phenomena (DLP) for model construction and it mimics processes
of the mind and natural evolution. This paper provides a formal description of
DLP by specifying its syntax, semantics, and reasoning system. We also outline
links between DLP and other logical approaches. Computational complexity issues
that motivate this work are presented using an example of polynomial models
Semantically-guided evolutionary knowledge discovery from texts
This thesis proposes a new approach for structured knowledge discovery from texts
which considers both the mining process itself, the evaluation of this knowledge by the
model, and the human assessment of the quality of the outcome.This is achieved by integrating Natural-Language technology and Genetic Algorithms to produce explanatory novel hypotheses. Natural-Language techniques are
specifically used to extract genre-based information from text documents. Additional
semantic and rhetorical information for generating training data and for feeding a semistructured Latent Semantic Analysis process is also captured.The discovery process is modeled by a semantically-guided Genetic Algorithm
which uses training data to guide the search and optimization process. A number of
novel criteria to evaluate the quality of the new knowledge are proposed. Consequently,
new genetic operations suitable for text mining are designed, and techniques for Evolutionary Multi-Objective Optimization are adapted for the model to trade off between
different criteria in the hypotheses.Domain experts were used in an experiment to assess the quality of the hypotheses
produced by the model so as to establish their effectiveness in terms of novel and
interesting knowledge. The assessment showed encouraging results for the discovered
knowledge and for the correlation between the model and the human opinions
- …