38,246 research outputs found
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
Concept Extraction and Clustering for Topic Digital Library Construction
This paper is to introduce a new approach to build
topic digital library using concept extraction and
document clustering. Firstly, documents in a special
domain are automatically produced by document
classification approach. Then, the keywords of each
document are extracted using the machine learning
approach. The keywords are used to cluster the
documents subset. The clustered result is the taxonomy
of the subset. Lastly, the taxonomy is modified to the
hierarchical structure for user navigation by manual
adjustments. The topic digital library is constructed
after combining the full-text retrieval and hierarchical
navigation function
Improving Hypernymy Extraction with Distributional Semantic Classes
In this paper, we show how distributionally-induced semantic classes can be
helpful for extracting hypernyms. We present methods for inducing sense-aware
semantic classes using distributional semantics and using these induced
semantic classes for filtering noisy hypernymy relations. Denoising of
hypernyms is performed by labeling each semantic class with its hypernyms. On
the one hand, this allows us to filter out wrong extractions using the global
structure of distributionally similar senses. On the other hand, we infer
missing hypernyms via label propagation to cluster terms. We conduct a
large-scale crowdsourcing study showing that processing of automatically
extracted hypernyms using our approach improves the quality of the hypernymy
extraction in terms of both precision and recall. Furthermore, we show the
utility of our method in the domain taxonomy induction task, achieving the
state-of-the-art results on a SemEval'16 task on taxonomy induction.Comment: In Proceedings of the 11th Conference on Language Resources and
Evaluation (LREC 2018). Miyazaki, Japa
Dutch hypernym detection : does decompounding help?
This research presents experiments carried out to improve the precision and recall of Dutch hypernym detection. To do so, we applied a data-driven semantic relation finder that starts from a list of automatically extracted domain-specific terms from technical corpora, and generates a list of hypernym relations between these terms. As Dutch technical terms often consist of compounds written in one orthographic unit, we investigated the impact of a decompounding module on the performance of the hypernym detection system.
In addition, we also improved the precision of the system by designing filters taking into account statistical and linguistic information.
The experimental results show that both the precision and recall of the hypernym detection system improved, and that the decompounding module is especially effective for hypernym detection in Dutch
ExTaSem! Extending, Taxonomizing and Semantifying Domain Terminologies
We introduce EXTASEM!, a novel approach for the automatic learning of lexical taxonomies from domain terminologies. First, we exploit a very large semantic network to collect thousands of in-domain textual definitions. Second, we extract (hyponym, hypernym) pairs from each definition with a CRF-based algorithm trained on manuallyvalidated data. Finally, we introduce a graph induction procedure which constructs a full-fledged taxonomy where each edge is weighted according to its domain pertinence. EXTASEM! achieves state-of-the-art results in the following taxonomy evaluation experiments: (1) Hypernym discovery, (2) Reconstructing gold standard taxonomies, and (3) Taxonomy quality according to structural measures. We release weighted taxonomies for six domains for the use and scrutiny of the communit
SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)
This paper describes the second edition of the shared task on Taxonomy Extraction Evaluation organised as part of SemEval 2016. This task aims to extract hypernym-hyponym relations between a given list of domain-specific terms and then to construct a domain taxonomy based on them. TExEval-2 introduced a multilingual setting for this task, covering four different languages including English, Dutch, Italian and French from domains as diverse as environment, food and science. A total of
62 runs submitted by 5 different teams were
evaluated using structural measures, by comparison with gold standard taxonomies and by manual quality assessment of novel relations.Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (INSIGHT
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