57 research outputs found
Bootstrapping a Tagged Corpus through Combination of Existing Heterogeneous Taggers
This paper describes a new method, Combi-bootstrap, to exploit existing
taggers and lexical resources for the annotation of corpora with new tagsets.
Combi-bootstrap uses existing resources as features for a second level machine
learning module, that is trained to make the mapping to the new tagset on a
very small sample of annotated corpus material. Experiments show that
Combi-bootstrap: i) can integrate a wide variety of existing resources, and ii)
achieves much higher accuracy (up to 44.7 % error reduction) than both the best
single tagger and an ensemble tagger constructed out of the same small training
sample.Comment: 4 page
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
e^+e^- Annihilations into Quasi-two-body Final States at 10.58 GeV
We report the first observation of annihilations into hadronic
states of positive -parity, and . The angular
distributions support two-virtual-photon annihilation production. We also
report the observations of and a preliminary result on
.Comment: Invited talk, 7 pages, 4 postscript figures, contributed to the
Workshop on Exclusive Reactions at High Momentum Transfer, 21-24 May 2007,
Jla
MBT: A Memory-Based Part of Speech Tagger-Generator
We introduce a memory-based approach to part of speech tagging. Memory-based
learning is a form of supervised learning based on similarity-based reasoning.
The part of speech tag of a word in a particular context is extrapolated from
the most similar cases held in memory. Supervised learning approaches are
useful when a tagged corpus is available as an example of the desired output of
the tagger. Based on such a corpus, the tagger-generator automatically builds a
tagger which is able to tag new text the same way, diminishing development time
for the construction of a tagger considerably. Memory-based tagging shares this
advantage with other statistical or machine learning approaches. Additional
advantages specific to a memory-based approach include (i) the relatively small
tagged corpus size sufficient for training, (ii) incremental learning, (iii)
explanation capabilities, (iv) flexible integration of information in case
representations, (v) its non-parametric nature, (vi) reasonably good results on
unknown words without morphological analysis, and (vii) fast learning and
tagging. In this paper we show that a large-scale application of the
memory-based approach is feasible: we obtain a tagging accuracy that is on a
par with that of known statistical approaches, and with attractive space and
time complexity properties when using {\em IGTree}, a tree-based formalism for
indexing and searching huge case bases.} The use of IGTree has as additional
advantage that optimal context size for disambiguation is dynamically computed.Comment: 14 pages, 2 Postscript figure
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
Part of Speech Tagging and Lemmatisation for the Spoken Dutch Corpus
Abstract This paper describes the lemmatisation and tagging guidelines developed for the "Spoken Dutch Corpus", and lays out the philosophy behind the high granularity tagset that was designed for the project. To bootstrap the annotation of large quantities of material (10 million words) with this new tagset we tested several existing taggers and tagger generators on initial samples of the corpus. The results show that the most effective method, when trained on the small samples, is a high quality implementation of a Hidden Markov Model tagger generator
Theoretical and Practical Design Approach of Wireless Power Systems
The paper introduces the main issues concerned with the conceptual design process of wireless power systems. It analyses the electromagnetic design of the inductive magnetic coupler and proposes the key formulas to optimize its electrical parameters for a particular load. For this purpose, a very detailed analysis is given focusing on the mathematical concept procedure for determination of the key factors influencing proper coupling coils design. It also suggests basic topologies for conceptual design of power electronics and discusses its proper connection to the grid. The proposed design strategy is verified by experimental laboratory measurement including analyses of leakage magnetic field
InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
Recently, InPars introduced a method to efficiently use large language models
(LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced
to generate relevant queries for documents. These synthetic query-document
pairs can then be used to train a retriever. However, InPars and, more
recently, Promptagator, rely on proprietary LLMs such as GPT-3 and FLAN to
generate such datasets. In this work we introduce InPars-v2, a dataset
generator that uses open-source LLMs and existing powerful rerankers to select
synthetic query-document pairs for training. A simple BM25 retrieval pipeline
followed by a monoT5 reranker finetuned on InPars-v2 data achieves new
state-of-the-art results on the BEIR benchmark. To allow researchers to further
improve our method, we open source the code, synthetic data, and finetuned
models: https://github.com/zetaalphavector/inPars/tree/master/tp
An Empirical Re-Examination of Weighted Voting for k-NN
For some applications of k-nearest neighbor classifiers, the best results are obtained at a relatively large value of k. With the majority voting method, these results can be suboptimal. In this paper the performance of various weighted voting methods is tested on a number of machine learning datasets. The results show that weighted voting is often superior to majority voting, and that the linear weighting function proposed by Dudani [5] often yields slightly better results than the inverse distance function that has commonly been used in more recent work. 1 Introduction Classification algorithms from the family of k-nearest neighbors (k-NN) [6] [4] or Instance Based Learning [1] [13] [12] are based on the idea that similar instances of a problem tend to have similar solutions. The basic algorithm stores a set of classified cases, represented by feature-value vectors, in memory. When a new case -- the query -- is to be classified, the k vectors with the smallest distance to it are se..
Feature-rich memory-based classification for shallow nlp and information extraction
Abstract. Memory-Based Learning (MBL) is based on the storage of all available training data, and similarity-based reasoning for handling new cases. By interpreting tasks such as POS tagging and shallow parsing as classification tasks, the advantages of MBL (implicit smoothing of sparse data, automatic integration and relevance weighting of information sources, handling exceptional data) contribute to state-of-the-art accuracy. However, Hidden Markov Models (HMM) typically achieve higher accuracy than MBL (and other Machine Learning approaches) for tasks such as POS tagging and chunking. In this paper, we investigate how the advantages of MBL, such as its potential to integrate various sources of information, come to play when we compare our approach to HMMs on two Information Extraction (IE) datasets: the well-known Seminar Announcement data set and a new German Curriculum Vitae data set. 1 Memory-Based Language Processing Memory-Based Learning (MBL) is a supervised classification-based learning method. A vector of feature values (an instance) is associated with a class by
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