71 research outputs found
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
FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language Model
Lexical substitutes have found use in areas such as paraphrasing, text
simplification, machine translation, word sense disambiguation, and part of
speech induction. However the computational complexity of accurately
identifying the most likely substitutes for a word has made large scale
experiments difficult. In this paper I introduce a new search algorithm,
FASTSUBS, that is guaranteed to find the K most likely lexical substitutes for
a given word in a sentence based on an n-gram language model. The computation
is sub-linear in both K and the vocabulary size V. An implementation of the
algorithm and a dataset with the top 100 substitutes of each token in the WSJ
section of the Penn Treebank are available at http://goo.gl/jzKH0.Comment: 4 pages, 1 figure, to appear in IEEE Signal Processing Letter
From genetic algorthms to efficient optimization
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 51-53).by Deniz Yuret.M.S
Identity-Aware Semi-Supervised Learning for Comic Character Re-Identification
Character re-identification, recognizing characters consistently across
different panels in comics, presents significant challenges due to limited
annotated data and complex variations in character appearances. To tackle this
issue, we introduce a robust semi-supervised framework that combines metric
learning with a novel 'Identity-Aware' self-supervision method by contrastive
learning of face and body pairs of characters. Our approach involves processing
both facial and bodily features within a unified network architecture,
facilitating the extraction of identity-aligned character embeddings that
capture individual identities while preserving the effectiveness of face and
body features. This integrated character representation enhances feature
extraction and improves character re-identification compared to
re-identification by face or body independently, offering a parameter-efficient
solution. By extensively validating our method using in-series and inter-series
evaluation metrics, we demonstrate its effectiveness in consistently
re-identifying comic characters. Compared to existing methods, our approach not
only addresses the challenge of character re-identification but also serves as
a foundation for downstream tasks since it can produce character embeddings
without restrictions of face and body availability, enriching the comprehension
of comic books. In our experiments, we leverage two newly curated datasets: the
'Comic Character Instances Dataset', comprising over a million character
instances and the 'Comic Sequence Identity Dataset', containing annotations of
identities within more than 3000 sets of four consecutive comic panels that we
collected.Comment: 18 pages, 9 Figure
BiLingUNet: Image Segmentation by Modulating Top-Down and Bottom-Up Visual Processing with Referring Expressions
We present BiLingUNet, a state-of-the-art model for image segmentation using
referring expressions. BiLingUNet uses language to customize visual filters and
outperforms approaches that concatenate a linguistic representation to the
visual input. We find that using language to modulate both bottom-up and
top-down visual processing works better than just making the top-down
processing language-conditional. We argue that common 1x1 language-conditional
filters cannot represent relational concepts and experimentally demonstrate
that wider filters work better. Our model achieves state-of-the-art performance
on four referring expression datasets.Comment: 18 pages, 3 figures, submitted to ECCV 202
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