54 research outputs found
Using Parallel Texts and Lexicons for Verbal Word Sense Disambiguation
We present a system for verbal Word Sense Disambiguation (WSD) that is able to exploit additional information from parallel texts and lexicons. It is an extension of our previous WSD method, which gave promising results but used only monolingual features. In the follow-up work described here, we have explored two additional ideas: using English-Czech bilingual resources (as features only - the task itself remains a monolingual WSD task), and using a 'hybrid' approach, adding features extracted both from a parallel corpus and from manually aligned bilingual valency lexicon entries, which contain subcategorization information. Albeit not all types of features proved useful, both ideas and additions have led to significant improvements for both languages explored
New Language Pairs in TectoMT
The TectoMT tree-to-tree machine translation system has been updated this year to support easier retraining for more translation directions. We use multilingual standards for morphology and syntax annotation and language-independent base rules. We include a simple, non-parametric way of combining TectoMT’s transfer model outputs
Translation of "It" in a Deep Syntax Framework
We present a novel approach to the translation of the English personal pronoun it to Czech. We conduct a linguistic analysis on how the distinct categories of it are usually mapped to their Czech counterparts. Armed with these observations, we design a discriminative translation model of it, which is then integrated into the TectoMT deep syntax MT framework. Features in the model take advantage of rich syntactic annotation TectoMT is based on, external
tools for anaphoricity resolution, lexical co-occurrence frequencies measured on a large parallel corpus and gold coreference annotation. Even though the new model for it exhibits no improvement in terms of BLEU, manual evaluation shows that it outperforms the original solution in
8.5% sentences containing it
Cross-lingual Coreference Resolution of Pronouns
This work is, to our knowledge, a first attempt at a machine learning approach to cross-lingual
coreference resolution, i.e. coreference resolution (CR) performed on a bitext. Focusing on CR of English pronouns, we leverage language differences and enrich the feature set of a standard monolingual CR system for English with features extracted from the Czech side of the bitext. Our work also includes a supervised pronoun aligner that outperforms a GIZA++ baseline in terms of both intrinsic evaluation and evaluation on CR. The final cross-lingual CR system has successfully outperformed both a monolingual CR and a cross-lingual projection system
Deep Multitask Learning for Semantic Dependency Parsing
We present a deep neural architecture that parses sentences into three
semantic dependency graph formalisms. By using efficient, nearly arc-factored
inference and a bidirectional-LSTM composed with a multi-layer perceptron, our
base system is able to significantly improve the state of the art for semantic
dependency parsing, without using hand-engineered features or syntax. We then
explore two multitask learning approaches---one that shares parameters across
formalisms, and one that uses higher-order structures to predict the graphs
jointly. We find that both approaches improve performance across formalisms on
average, achieving a new state of the art. Our code is open-source and
available at https://github.com/Noahs-ARK/NeurboParser.Comment: Proceedings of ACL 201
Coreference chains in Czech, English and Russian: Preliminary findings
Tento článek je pilotní srovnavací výzkum koreferenčních řetězců v češtině, angličtině a ruštině. Podrobili jsme analýze 16 srovnatelných textů ve třech jazycích. Naší motivací bylo zjistit lingvistickou strukturu koreferenčních řetězců v těchto jazycích a určit, které faktory ovlivňují tuto strukturu
Difference between written and spoken Czech::The case of verbal nouns denoting an action
Abstract
The present paper extends understanding of differences in expressing actions by verbal nouns in corpora of written vs. spoken Czech, namely in the Czech part of the Prague Czech-English Dependency Treebank and in the Prague Dependency Treebank of Spoken Czech.
We show that while the written corpus includes more complex noun phrases with more explicit expression of adnominal participants, noun phrases in the spoken corpus contain more deletions and more exophoric references. We also carried out a quantitative analysis focusing on relative frequencies of combinations of participants modifying verbal nouns; although the written corpus shows higher relative frequencies, the order of the relative frequencies of particular combinations is the same in both types of communication.</jats:p
Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation
In this paper, we empirically evaluate the utility of transfer and multi-task
learning on a challenging semantic classification task: semantic interpretation
of noun--noun compounds. Through a comprehensive series of experiments and
in-depth error analysis, we show that transfer learning via parameter
initialization and multi-task learning via parameter sharing can help a neural
classification model generalize over a highly skewed distribution of relations.
Further, we demonstrate how dual annotation with two distinct sets of relations
over the same set of compounds can be exploited to improve the overall accuracy
of a neural classifier and its F1 scores on the less frequent, but more
difficult relations.Comment: EMNLP 2018: Conference on Empirical Methods in Natural Language
Processing (EMNLP
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