12,982 research outputs found
Forgetting Exceptions is Harmful in Language Learning
We show that in language learning, contrary to received wisdom, keeping
exceptional training instances in memory can be beneficial for generalization
accuracy. We investigate this phenomenon empirically on a selection of
benchmark natural language processing tasks: grapheme-to-phoneme conversion,
part-of-speech tagging, prepositional-phrase attachment, and base noun phrase
chunking. In a first series of experiments we combine memory-based learning
with training set editing techniques, in which instances are edited based on
their typicality and class prediction strength. Results show that editing
exceptional instances (with low typicality or low class prediction strength)
tends to harm generalization accuracy. In a second series of experiments we
compare memory-based learning and decision-tree learning methods on the same
selection of tasks, and find that decision-tree learning often performs worse
than memory-based learning. Moreover, the decrease in performance can be linked
to the degree of abstraction from exceptions (i.e., pruning or eagerness). We
provide explanations for both results in terms of the properties of the natural
language processing tasks and the learning algorithms.Comment: 31 pages, 7 figures, 10 tables. uses 11pt, fullname, a4wide tex
styles. Pre-print version of article to appear in Machine Learning 11:1-3,
Special Issue on Natural Language Learning. Figures on page 22 slightly
compressed to avoid page overloa
Do not forget: Full memory in memory-based learning of word pronunciation
Memory-based learning, keeping full memory of learning material, appears a
viable approach to learning NLP tasks, and is often superior in generalisation
accuracy to eager learning approaches that abstract from learning material.
Here we investigate three partial memory-based learning approaches which remove
from memory specific task instance types estimated to be exceptional. The three
approaches each implement one heuristic function for estimating exceptionality
of instance types: (i) typicality, (ii) class prediction strength, and (iii)
friendly-neighbourhood size. Experiments are performed with the memory-based
learning algorithm IB1-IG trained on English word pronunciation. We find that
removing instance types with low prediction strength (ii) is the only tested
method which does not seriously harm generalisation accuracy. We conclude that
keeping full memory of types rather than tokens, and excluding minority
ambiguities appear to be the only performance-preserving optimisations of
memory-based learning.Comment: uses conll98, epsf, and ipamacs (WSU IPA
Combined optimization of feature selection and algorithm parameters in machine learning of language
Comparative machine learning experiments have become an important methodology in empirical approaches to natural language processing (i) to investigate which machine learning algorithms have the 'right bias' to solve specific natural language processing tasks, and (ii) to investigate which sources of information add to accuracy in a learning approach. Using automatic word sense disambiguation as an example task, we show that with the methodology currently used in comparative machine learning experiments, the results may often not be reliable because of the role of and interaction between feature selection and algorithm parameter optimization. We propose genetic algorithms as a practical approach to achieve both higher accuracy within a single approach, and more reliable comparisons
Sometimes less is more : Romanian word sense disambiguation revisited
Recent approaches to Word Sense Disambiguation (WSD) generally fall into two classes: (1) information-intensive approaches and (2) information-poor approaches. Our hypothesis is that for memory-based learning (MBL), a reduced amount of data is more beneficial than the full range of features used in the past. Our experiments show that MBL combined with a restricted set of features and a feature selection method that minimizes the feature set leads to competitive results, outperforming all systems that participated in the SENSEVAL-3 competition on the Romanian data. Thus, with this specific method, a tightly controlled feature set improves the accuracy of the classifier, reaching 74.0% in the fine-grained and 78.7% in the coarse-grained evaluation
Memory-based vocalization of Arabic
The problem of vocalization, or diacritization, is essential to many tasks in Arabic NLP. Arabic is generally written without the short vowels, which leads to one written form having several pronunciations with each pronunciation carrying its own meaning(s). In the experiments reported here, we define vocalization as a classification problem in which we decide for each character in the unvocalized word whether it is followed by a short vowel. We investigate the importance of different types of context. Our results show that the combination of using memory-based learning with only a word internal context leads to a word error rate of 6.64%. If a lexical context is added, the results deteriorate slowly
Phase transition in a sexual age-structured model of learning foreign languages
The understanding of language competition helps us to predict extinction and
survival of languages spoken by minorities. A simple agent-based model of a
sexual population, based on the Penna model, is built in order to find out
under which circumstances one language dominates other ones. This model
considers that only young people learn foreign languages. The simulations show
a first order phase transition where the ratio between the number of speakers
of different languages is the order parameter and the mutation rate is the
control one.Comment: preliminary version, to be submitted to Int. J. Mod. Phys.
Memory-Based Shallow Parsing
We present a memory-based learning (MBL) approach to shallow parsing in which
POS tagging, chunking, and identification of syntactic relations are formulated
as memory-based modules. The experiments reported in this paper show
competitive results, the F-value for the Wall Street Journal (WSJ) treebank is:
93.8% for NP chunking, 94.7% for VP chunking, 77.1% for subject detection and
79.0% for object detection.Comment: 8 pages, to appear in: Proceedings of the EACL'99 workshop on
Computational Natural Language Learning (CoNLL-99), Bergen, Norway, June 199
- …