In this paper we present a novel resource-inexpensive architecture for metaphor detection based on a residual bidirectional long short-term memory and conditional random fields. Current approaches on this task rely on deep neural networks to identify metaphorical words, using additional linguistic features or word embeddings. We evaluate our proposed approach using different model configurations that combine embeddings, part of speech tags, and semantically disambiguated synonym sets. This evaluation process was performed using the training and testing partitions of the VU Amsterdam Metaphor Corpus. We use this method of evaluation as reference to compare the results with other current neural approaches for this task that implement similar neural architectures and features, and that were evaluated using this corpus. Results show that our system achieves competitive results with a simpler architecture compared to previous approaches.

In this paper we present the evaluation results for the creation of WordNets for five languages (Spanish, French, German, Italian and Portuguese) using an approach based on parallel corpora. We have used three very large parallel corpora for our experiments: DGT-TM, EMEA and ECB. The English part of each corpus is semantically tagged using Freeling and UKB. After this step, the process of WordNet creation is converted into a word alignment problem, where we want to alignWordNet synsets in the English part of the corpus with lemmata on the target language part of the corpus. The word alignment algorithm used in these experiments is a simple most frequent translation algorithm implemented into the WN-Toolkit. The obtained precision values are quite satisfactory, but the overall number of extracted synset-variant pairs is too low, leading into very poor recall values. In the conclusions, the use of more advanced word alignment algorithms, such as Giza++, Fast Align or Berkeley aligner is suggested.

Machine translationness (MTness) is the linguistic phenomena that make machine translations distinguishable from human translations. This paper intends to present MTness as a research object and suggests an MT evaluation method based on determining whether the translation is machine-like instead of determining its human-likeness as in evaluation current approaches. The method rates the MTness of a translation with a metric, the MTS (Machine Translationness Score). The MTS calculation is in accordance with the results of an experimental study on machine translation perception by common people. MTS proved to correlate well with human ratings on translation quality. Besides, our approach allows the performance of cheap evaluations since expensive resources (e.g. reference translations, training corpora) are not needed. The paper points out the challenge of dealing with MTness as an everyday phenomenon caused by the massive use of MT.

This paper presents the complete and consistent ontological annotation of the nominal part of WordNet. The annotation has been carried out using the semantic features defined in the EuroWordNet Top Concept Ontology and made available to the NLP community. Up to now only an initial core set of 1,024 synsets, the so-called Base Concepts, was ontologized in such a way. The work has been achieved by following a methodology based on an iterative and incremental expansion of the initial labeling through the hierarchy while setting inheritance blockage points. Since this labeling has been set on the EuroWordNet’s Interlingual Index (ILI), it can be also used to populate any other wordnet linked to it through a simple porting process. This feature-annotated WordNet is intended to be useful for a large number of semantic NLP tasks and for testing for the first time componential analysis on real environments. Moreover, the quantitative analysis of the work shows that more than 40% of the nominal part of WordNet is involved in structure errors or inadequacies.

We present the results of an agreement task carried out in the framework of the KNOW Project and consisting in manually annotating an agreement sample totaling 50 sentences extracted from the SenSem corpus. Diambiguation was carried out for all nouns, proper nouns and adjectives in the sample, all of which were assigned EuroWordNet (EWN) synsets. As a result of the task, Spanish WN has been shown to exhibit 1) lack of explanatory clarity (it does not define word meanings, but glosses and examplifies them instead; it does not systematically encode metaphoric meanings, either); 2) structural inadequacy (some words appear as hyponyms of another sense of the same word; sometimes there even coexist in Spanish WN a general sense and a specific one related to the same concept, but with no structural link in between; hyperonymy relationships have been detected that are likely to raise doubts to human annotators; there can even be found cases of auto-hyponymy); 3) cross-linguistic inconsistency (there exist in English EWN concepts whose lexical equivalent is missing in Spanish WN; glosses in one language more often than not contradict or diverge from glosses in another language).