1,990 research outputs found
Tagging a Norwegian Speech Corpus
Proceedings of the 16th Nordic Conference
of Computational Linguistics NODALIDA-2007.
Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit.
University of Tartu, Tartu, 2007.
ISBN 978-9985-4-0513-0 (online)
ISBN 978-9985-4-0514-7 (CD-ROM)
pp. 245-248
External Lexical Information for Multilingual Part-of-Speech Tagging
Morphosyntactic lexicons and word vector representations have both proven
useful for improving the accuracy of statistical part-of-speech taggers. Here
we compare the performances of four systems on datasets covering 16 languages,
two of these systems being feature-based (MEMMs and CRFs) and two of them being
neural-based (bi-LSTMs). We show that, on average, all four approaches perform
similarly and reach state-of-the-art results. Yet better performances are
obtained with our feature-based models on lexically richer datasets (e.g. for
morphologically rich languages), whereas neural-based results are higher on
datasets with less lexical variability (e.g. for English). These conclusions
hold in particular for the MEMM models relying on our system MElt, which
benefited from newly designed features. This shows that, under certain
conditions, feature-based approaches enriched with morphosyntactic lexicons are
competitive with respect to neural methods
Active Learning for Dialogue Act Classification
Active learning techniques were employed for classification of dialogue acts over two dialogue corpora, the English human-human Switchboard corpus and the Spanish human-machine Dihana corpus. It is shown clearly that active learning improves on a baseline obtained through a passive learning approach to tagging the same data sets. An error reduction of 7% was obtained on Switchboard, while a factor 5 reduction in the amount of labeled data needed for classification was achieved on Dihana. The passive Support Vector Machine learner used as baseline in itself significantly improves the state of the art in dialogue act classification on both corpora. On Switchboard it gives a 31% error reduction compared to the previously best reported result
An improved neural network model for joint POS tagging and dependency parsing
We propose a novel neural network model for joint part-of-speech (POS)
tagging and dependency parsing. Our model extends the well-known BIST
graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating
a BiLSTM-based tagging component to produce automatically predicted POS tags
for the parser. On the benchmark English Penn treebank, our model obtains
strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+%
absolute improvements to the BIST graph-based parser, and also obtaining a
state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental
results on parsing 61 "big" Universal Dependencies treebanks from raw texts
show that our model outperforms the baseline UDPipe (Straka and Strakov\'a,
2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS
score. In addition, with our model, we also obtain state-of-the-art downstream
task scores for biomedical event extraction and opinion analysis applications.
Our code is available together with all pre-trained models at:
https://github.com/datquocnguyen/jPTDPComment: 11 pages; In Proceedings of the CoNLL 2018 Shared Task: Multilingual
Parsing from Raw Text to Universal Dependencies, to appea
Methods for Amharic part-of-speech tagging
The paper describes a set of experiments
involving the application of three state-of-
the-art part-of-speech taggers to Ethiopian
Amharic, using three different tagsets.
The taggers showed worse performance
than previously reported results for Eng-
lish, in particular having problems with
unknown words. The best results were
obtained using a Maximum Entropy ap-
proach, while HMM-based and SVM-
based taggers got comparable results
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