18,551 research outputs found
Learning to Disambiguate Syntactic Relations
Natural Language is highly ambiguous, on every level. This article describes a fast broad-coverage state-of-the-art parser that uses a carefully hand-written grammar and probability-based machine learning approaches on the syntactic level. It is shown in detail which statistical learning models based on Maximum-Likelihood Estimation (MLE) can support a highly developed linguistic grammar in the disambiguation process
Learning to Disambiguate Syntactic Relations
Many extensions to text-based, data-intensive knowledge management approaches, such as Information Retrieval or Data Mining, focus on integrating the impressive recent advances in language technology. For this, they need fast, robust parsers that deliver linguistic data which is meaningful for the subsequent processing stages. This paper introduces such a parsing system and discusses some of its disambiguation techniques which are based on learning from a large syntactically annotated corpus.
The paper is organized as follows. Section 2 explains the motivations for writing the parser, and why it profits from Dependency grammar assumptions. Section 3 gives a brief introduction to the parsing system and to evaluation questions. Section 4 presents the probabilistic models and the conducted experiments in detail
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation
The success of deep learning methods hinges on the availability of large
training datasets annotated for the task of interest. In contrast to human
intelligence, these methods lack versatility and struggle to learn and adapt
quickly to new tasks, where labeled data is scarce. Meta-learning aims to solve
this problem by training a model on a large number of few-shot tasks, with an
objective to learn new tasks quickly from a small number of examples. In this
paper, we propose a meta-learning framework for few-shot word sense
disambiguation (WSD), where the goal is to learn to disambiguate unseen words
from only a few labeled instances. Meta-learning approaches have so far been
typically tested in an -way, -shot classification setting where each task
has classes with examples per class. Owing to its nature, WSD deviates
from this controlled setup and requires the models to handle a large number of
highly unbalanced classes. We extend several popular meta-learning approaches
to this scenario, and analyze their strengths and weaknesses in this new
challenging setting.Comment: Added additional experiment
Doppelgangers: Learning to Disambiguate Images of Similar Structures
We consider the visual disambiguation task of determining whether a pair of
visually similar images depict the same or distinct 3D surfaces (e.g., the same
or opposite sides of a symmetric building). Illusory image matches, where two
images observe distinct but visually similar 3D surfaces, can be challenging
for humans to differentiate, and can also lead 3D reconstruction algorithms to
produce erroneous results. We propose a learning-based approach to visual
disambiguation, formulating it as a binary classification task on image pairs.
To that end, we introduce a new dataset for this problem, Doppelgangers, which
includes image pairs of similar structures with ground truth labels. We also
design a network architecture that takes the spatial distribution of local
keypoints and matches as input, allowing for better reasoning about both local
and global cues. Our evaluation shows that our method can distinguish illusory
matches in difficult cases, and can be integrated into SfM pipelines to produce
correct, disambiguated 3D reconstructions. See our project page for our code,
datasets, and more results: http://doppelgangers-3d.github.io/.Comment: Published in ICCV 2023 (Oral); Project page:
http://doppelgangers-3d.github.io
A role for the developing lexicon in phonetic category acquisition
Infants segment words from fluent speech during the same period when they are learning phonetic categories, yet accounts of phonetic category acquisition typically ignore information about the words in which sounds appear. We use a Bayesian model to illustrate how feedback from segmented words might constrain phonetic category learning by providing information about which sounds occur together in words. Simulations demonstrate that word-level information can successfully disambiguate overlapping English vowel categories. Learning patterns in the model are shown to parallel human behavior from artificial language learning tasks. These findings point to a central role for the developing lexicon in phonetic category acquisition and provide a framework for incorporating top-down constraints into models of category learning
Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning
This paper describes an experimental comparison of seven different learning
algorithms on the problem of learning to disambiguate the meaning of a word
from context. The algorithms tested include statistical, neural-network,
decision-tree, rule-based, and case-based classification techniques. The
specific problem tested involves disambiguating six senses of the word ``line''
using the words in the current and proceeding sentence as context. The
statistical and neural-network methods perform the best on this particular
problem and we discuss a potential reason for this observed difference. We also
discuss the role of bias in machine learning and its importance in explaining
performance differences observed on specific problems.Comment: 10 page
Secost: Sequential co-supervision for large scale weakly labeled audio event detection
Weakly supervised learning algorithms are critical for scaling audio event
detection to several hundreds of sound categories. Such learning models should
not only disambiguate sound events efficiently with minimal class-specific
annotation but also be robust to label noise, which is more apparent with weak
labels instead of strong annotations. In this work, we propose a new framework
for designing learning models with weak supervision by bridging ideas from
sequential learning and knowledge distillation. We refer to the proposed
methodology as SeCoST (pronounced Sequest) -- Sequential Co-supervision for
training generations of Students. SeCoST incrementally builds a cascade of
student-teacher pairs via a novel knowledge transfer method. Our evaluations on
Audioset (the largest weakly labeled dataset available) show that SeCoST
achieves a mean average precision of 0.383 while outperforming prior state of
the art by a considerable margin.Comment: Accepted IEEE ICASSP 202
Word sense disambiguation criteria: a systematic study
This article describes the results of a systematic in-depth study of the
criteria used for word sense disambiguation. Our study is based on 60 target
words: 20 nouns, 20 adjectives and 20 verbs. Our results are not always in line
with some practices in the field. For example, we show that omitting
non-content words decreases performance and that bigrams yield better results
than unigrams
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