4,647 research outputs found
Similarity-Based Models of Word Cooccurrence Probabilities
In many applications of natural language processing (NLP) it is necessary to
determine the likelihood of a given word combination. For example, a speech
recognizer may need to determine which of the two word combinations ``eat a
peach'' and ``eat a beach'' is more likely. Statistical NLP methods determine
the likelihood of a word combination from its frequency in a training corpus.
However, the nature of language is such that many word combinations are
infrequent and do not occur in any given corpus. In this work we propose a
method for estimating the probability of such previously unseen word
combinations using available information on ``most similar'' words.
We describe probabilistic word association models based on distributional
word similarity, and apply them to two tasks, language modeling and pseudo-word
disambiguation. In the language modeling task, a similarity-based model is used
to improve probability estimates for unseen bigrams in a back-off language
model. The similarity-based method yields a 20% perplexity improvement in the
prediction of unseen bigrams and statistically significant reductions in
speech-recognition error.
We also compare four similarity-based estimation methods against back-off and
maximum-likelihood estimation methods on a pseudo-word sense disambiguation
task in which we controlled for both unigram and bigram frequency to avoid
giving too much weight to easy-to-disambiguate high-frequency configurations.
The similarity-based methods perform up to 40% better on this particular task.Comment: 26 pages, 5 figure
Language modeling using X-grams
In this paper, an extension of n-grams, called x-grams, is proposed. In this extension, the memory of the model (n) is not fixed a priori. Instead, large memories are accepted first, and merging criteria are then applied to reduce the complexity and to ensure reliable estimations. The results show how the perplexity obtained with x-grams is smaller than that of n-grams. Furthermore, the complexity is smaller than trigrams and can become close to bigrams.Peer ReviewedPostprint (published version
Language Modeling with Power Low Rank Ensembles
We present power low rank ensembles (PLRE), a flexible framework for n-gram
language modeling where ensembles of low rank matrices and tensors are used to
obtain smoothed probability estimates of words in context. Our method can be
understood as a generalization of n-gram modeling to non-integer n, and
includes standard techniques such as absolute discounting and Kneser-Ney
smoothing as special cases. PLRE training is efficient and our approach
outperforms state-of-the-art modified Kneser Ney baselines in terms of
perplexity on large corpora as well as on BLEU score in a downstream machine
translation task
Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic
Language modeling for an inflected language
such as Arabic poses new challenges for speech recognition and
machine translation due to its rich morphology. Rich morphology
results in large increases in out-of-vocabulary (OOV) rate and
poor language model parameter estimation in the absence of large
quantities of data. In this study, we present a joint
morphological-lexical language model (JMLLM) that takes
advantage of Arabic morphology. JMLLM combines
morphological segments with the underlying lexical items and
additional available information sources with regards to
morphological segments and lexical items in a single joint model.
Joint representation and modeling of morphological and lexical
items reduces the OOV rate and provides smooth probability
estimates while keeping the predictive power of whole words.
Speech recognition and machine translation experiments in
dialectal-Arabic show improvements over word and morpheme
based trigram language models. We also show that as the
tightness of integration between different information sources
increases, both speech recognition and machine translation
performances improve
An empirical analysis of phrase-based and neural machine translation
Two popular types of machine translation (MT) are phrase-based and neural
machine translation systems. Both of these types of systems are composed of
multiple complex models or layers. Each of these models and layers learns
different linguistic aspects of the source language. However, for some of these
models and layers, it is not clear which linguistic phenomena are learned or
how this information is learned. For phrase-based MT systems, it is often clear
what information is learned by each model, and the question is rather how this
information is learned, especially for its phrase reordering model. For neural
machine translation systems, the situation is even more complex, since for many
cases it is not exactly clear what information is learned and how it is
learned.
To shed light on what linguistic phenomena are captured by MT systems, we
analyze the behavior of important models in both phrase-based and neural MT
systems. We consider phrase reordering models from phrase-based MT systems to
investigate which words from inside of a phrase have the biggest impact on
defining the phrase reordering behavior. Additionally, to contribute to the
interpretability of neural MT systems we study the behavior of the attention
model, which is a key component in neural MT systems and the closest model in
functionality to phrase reordering models in phrase-based systems. The
attention model together with the encoder hidden state representations form the
main components to encode source side linguistic information in neural MT. To
this end, we also analyze the information captured in the encoder hidden state
representations of a neural MT system. We investigate the extent to which
syntactic and lexical-semantic information from the source side is captured by
hidden state representations of different neural MT architectures.Comment: PhD thesis, University of Amsterdam, October 2020.
https://pure.uva.nl/ws/files/51388868/Thesis.pd
Memory-Based Learning: Using Similarity for Smoothing
This paper analyses the relation between the use of similarity in
Memory-Based Learning and the notion of backed-off smoothing in statistical
language modeling. We show that the two approaches are closely related, and we
argue that feature weighting methods in the Memory-Based paradigm can offer the
advantage of automatically specifying a suitable domain-specific hierarchy
between most specific and most general conditioning information without the
need for a large number of parameters. We report two applications of this
approach: PP-attachment and POS-tagging. Our method achieves state-of-the-art
performance in both domains, and allows the easy integration of diverse
information sources, such as rich lexical representations.Comment: 8 pages, uses aclap.sty, To appear in Proc. ACL/EACL 9
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