391 research outputs found
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
Tagset Reductions in Morphosyntactic Tagging of Croatian Texts
Morphosyntactic tagging of Croatian texts is performed with stochastic taggersby using a language model built on a manually annotated corpus implementingthe Multext East version 3 specifications for Croatian. Tagging accuracy in thisframework is basically predefined, i.e. proportionally dependent of two things:the size of the training corpus and the number of different morphosyntactic tagsencompassed by that corpus. Being that the 100 kw Croatia Weekly newspapercorpus by definition makes a rather small language model in terms of stochastictagging of free domain texts, the paper presents an approach dealing withtagset reductions. Several meaningful subsets of the Croatian Multext-East version3 morphosyntactic tagset specifications are created and applied on Croatiantexts with the CroTag stochastic tagger, measuring overall tagging accuracyand F1-measures. Obtained results are discussed in terms of applying differentreductions in different natural language processing systems and specifictasks defined by specific user requirements
Mimicking Word Embeddings using Subword RNNs
Word embeddings improve generalization over lexical features by placing each
word in a lower-dimensional space, using distributional information obtained
from unlabeled data. However, the effectiveness of word embeddings for
downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which
embeddings do not exist. In this paper, we present MIMICK, an approach to
generating OOV word embeddings compositionally, by learning a function from
spellings to distributional embeddings. Unlike prior work, MIMICK does not
require re-training on the original word embedding corpus; instead, learning is
performed at the type level. Intrinsic and extrinsic evaluations demonstrate
the power of this simple approach. On 23 languages, MIMICK improves performance
over a word-based baseline for tagging part-of-speech and morphosyntactic
attributes. It is competitive with (and complementary to) a supervised
character-based model in low-resource settings.Comment: EMNLP 201
Cross-Lingual Dependency Parsing for Closely Related Languages - Helsinki's Submission to VarDial 2017
This paper describes the submission from the University of Helsinki to the
shared task on cross-lingual dependency parsing at VarDial 2017. We present
work on annotation projection and treebank translation that gave good results
for all three target languages in the test set. In particular, Slovak seems to
work well with information coming from the Czech treebank, which is in line
with related work. The attachment scores for cross-lingual models even surpass
the fully supervised models trained on the target language treebank. Croatian
is the most difficult language in the test set and the improvements over the
baseline are rather modest. Norwegian works best with information coming from
Swedish whereas Danish contributes surprisingly little
A non-projective greedy dependency parser with bidirectional LSTMs
The LyS-FASTPARSE team presents BIST-COVINGTON, a neural implementation of
the Covington (2001) algorithm for non-projective dependency parsing. The
bidirectional LSTM approach by Kipperwasser and Goldberg (2016) is used to
train a greedy parser with a dynamic oracle to mitigate error propagation. The
model participated in the CoNLL 2017 UD Shared Task. In spite of not using any
ensemble methods and using the baseline segmentation and PoS tagging, the
parser obtained good results on both macro-average LAS and UAS in the big
treebanks category (55 languages), ranking 7th out of 33 teams. In the all
treebanks category (LAS and UAS) we ranked 16th and 12th. The gap between the
all and big categories is mainly due to the poor performance on four parallel
PUD treebanks, suggesting that some `suffixed' treebanks (e.g. Spanish-AnCora)
perform poorly on cross-treebank settings, which does not occur with the
corresponding `unsuffixed' treebank (e.g. Spanish). By changing that, we obtain
the 11th best LAS among all runs (official and unofficial). The code is made
available at https://github.com/CoNLL-UD-2017/LyS-FASTPARSEComment: 12 pages, 2 figures, 5 table
Data sparsity in highly inflected languages: the case of morphosyntactic tagging in Polish
In morphologically complex languages, many high-level tasks in natural language
processing rely on accurate morphosyntactic analyses of the input. However, in
light of the risk of error propagation in present-day pipeline architectures for basic
linguistic pre-processing, the state of the art for morphosyntactic tagging is still
not satisfactory. The main obstacle here is data sparsity inherent to natural lan-
guage in general and highly inflected languages in particular.
In this work, we investigate whether semi-supervised systems may alleviate the
data sparsity problem. Our approach uses word clusters obtained from large
amounts of unlabelled text in an unsupervised manner in order to provide a su-
pervised probabilistic tagger with morphologically informed features. Our evalua-
tions on a number of datasets for the Polish language suggest that this simple
technique improves tagging accuracy, especially with regard to out-of-vocabulary
words. This may prove useful to increase cross-domain performance of taggers,
and to alleviate the dependency on large amounts of supervised training data,
which is especially important from the perspective of less-resourced languages
Automatic Question Generation Using Semantic Role Labeling for Morphologically Rich Languages
In this paper, a novel approach to automatic question generation (AQG) using semantic role labeling (SRL) for morphologically rich languages is presented. A model for AQG is developed for our native speaking language, Croatian. Croatian language is a highly inflected language that belongs to Balto-Slavic family of languages. Globally this article can be divided into two stages. In the first stage we present a novel approach to SRL of texts written in Croatian language that uses Conditional Random Fields (CRF). SRL traditionally consists of predicate disambiguation, argument identification and argument classification. After these steps most approaches use beam search to find optimal sequence of arguments based on given predicate. We propose the architecture for predicate identification and argument classification in which finding the best sequence of arguments is handled by Viterbi decoding. We enrich SRL features with custom attributes that are custom made for this language. Our SRL system achieves F1 score of 78% in argument classification step on Croatian hr 500k corpus. In the second stage the proposed SRL model is used to develop AQG system for question generation from texts written in Croatian language. We proposed custom templates for AQG that were used to generate a total of 628 questions which were evaluated by experts scoring every question on a Likert scale. Expert evaluation of the system showed that our AQG achieved good results. The evaluation showed that 68% of the generated questions could be used for educational purposes. With these results the proposed AQG system could be used for possible implementation inside educational systems such as Intelligent Tutoring Systems
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