1,349 research outputs found
Boosting Named Entity Recognition with Neural Character Embeddings
Most state-of-the-art named entity recognition (NER) systems rely on
handcrafted features and on the output of other NLP tasks such as
part-of-speech (POS) tagging and text chunking. In this work we propose a
language-independent NER system that uses automatically learned features only.
Our approach is based on the CharWNN deep neural network, which uses word-level
and character-level representations (embeddings) to perform sequential
classification. We perform an extensive number of experiments using two
annotated corpora in two different languages: HAREM I corpus, which contains
texts in Portuguese; and the SPA CoNLL-2002 corpus, which contains texts in
Spanish. Our experimental results shade light on the contribution of neural
character embeddings for NER. Moreover, we demonstrate that the same neural
network which has been successfully applied to POS tagging can also achieve
state-of-the-art results for language-independet NER, using the same
hyperparameters, and without any handcrafted features. For the HAREM I corpus,
CharWNN outperforms the state-of-the-art system by 7.9 points in the F1-score
for the total scenario (ten NE classes), and by 7.2 points in the F1 for the
selective scenario (five NE classes).Comment: 9 page
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
Polyglot: Distributed Word Representations for Multilingual NLP
Distributed word representations (word embeddings) have recently contributed
to competitive performance in language modeling and several NLP tasks. In this
work, we train word embeddings for more than 100 languages using their
corresponding Wikipedias. We quantitatively demonstrate the utility of our word
embeddings by using them as the sole features for training a part of speech
tagger for a subset of these languages. We find their performance to be
competitive with near state-of-art methods in English, Danish and Swedish.
Moreover, we investigate the semantic features captured by these embeddings
through the proximity of word groupings. We will release these embeddings
publicly to help researchers in the development and enhancement of multilingual
applications.Comment: 10 pages, 2 figures, Proceedings of Conference on Computational
Natural Language Learning CoNLL'201
Simple data-driven context-sensitive lemmatization
Lemmatization for languages with rich inflectional morphology is one of the basic, indispensable steps in a language processing pipeline. In this paper we present a simple data-driven context-sensitive approach to lemmatizating word forms in running text. We treat lemmatization as a classification task for Machine Learning, and automatically induce class labels. We achieve this by computing a Shortest Edit Script (SES) between reversed input and output strings. A SES describes the transformations that have to be applied to the
input string (word form) in order to convert it to the output string (lemma). Our approach shows competitive performance on a range of typologically different languages
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
GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection
In this paper we present GumDrop, Georgetown University's entry at the DISRPT
2019 Shared Task on automatic discourse unit segmentation and connective
detection. Our approach relies on model stacking, creating a heterogeneous
ensemble of classifiers, which feed into a metalearner for each final task. The
system encompasses three trainable component stacks: one for sentence
splitting, one for discourse unit segmentation and one for connective
detection. The flexibility of each ensemble allows the system to generalize
well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking
(DISRPT2019
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