8,508 research outputs found
Universal dependencies for Turkish
The Universal Dependencies (UD) project was conceived after the substantial recent interest in unifying annotation schemes across languages. With its own annotation principles and abstract inventory for parts of speech, morphosyntactic features and dependency relations, UD aims to facilitate multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. This paper presents the Turkish IMST-UD Treebank, the first Turkish treebank to be in a UD release. The IMST-UD Treebank was automatically converted from the IMST Treebank, which was also recently released. We describe this conversion procedure in detail, complete with mapping tables. We also present our evaluation of the parsing performances of both versions of the IMST Treebank. Our findings suggest that the UD framework is at least as viable for Turkish as the original annotation framework of the IMST Treebank.Peer reviewe
Implementing universal dependency, morphology, and multiword expression annotation standards for Turkish language processing
Released only a year ago as the outputs of a research project (“Parsing Web 2.0 Sentences”, supported in part by a TUBİTAK 1001 grant (No. 112E276) and a part of the ICT COST Action PARSEME (IC1207)), IMST and IWT are currently the most comprehensive Turkish dependency treebanks in the literature. This article introduces the final states of our treebanks, as well as a newly integrated hierarchical categorization of the multiheaded dependencies and their organization in an exclusive deep dependency layer in the treebanks. It also presents the adaptation of recent studies on standardizing multiword expression and named entity annotation schemes for the Turkish language and integration of benchmark annotations into the dependency layers of our treebanks and the mapping of the treebanks to the latest Universal Dependencies (v2.0) standard, ensuring further compliance with rising universal annotation trends. In addition to significantly boosting the universal recognition of Turkish treebanks, our recent efforts have shown an improvement in their syntactic parsing performance (up to 77.8%/82.8% LAS and 84.0%/87.9% UAS for IMST/IWT, respectively). The final states of the treebanks are expected to be more suited to different natural language processing tasks, such as named entity recognition, multiword expression detection, transfer-based machine translation, semantic parsing, and semantic role labeling.Peer reviewe
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
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
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