Articles

AbstractThis paper evaluates different techniques for building a supervised, multilanguage keyphrase extraction pipeline for languages which lack a gold standard. Starting from an unsupervised English keyphrase extraction pipeline, we implement pipelines for Arabic, Italian, Portuguese, and Romanian, and we build test collections for languages which lack one. Then, we add …...

AbstractThis paper presents the development of a natural language inference engine that benefits from two current standard approaches; i.e., shallow and deep approaches. This system combines two non-deterministic algorithms: the approximate matching from the shallow approach and a theorem prover from the deep approach for handling multi-step inference tasks. The …...

AbstractThis paper presents an approach to generating common sense knowledge written in raw English sentences. Instead of using public contributors to feed this source, this system chose to employ expert linguistics decisions by using definitions from English dictionaries. Because the definitions in English dictionaries are not prepared to be transformed …...

AbstractA number of machine translation methods have been proposed in recent years to deal with the increasingly important problem of automatic translation between texts of different languages or languages and their dialects. These methods have produced promising results when applied to some of the widely studied languages. Existing translation methods …...

AbstractWe present GATE DictLemmatizer, a multilingual open source lemmatizer for the GATE NLP framework that currently supports English, German, Italian, French, Dutch, and Spanish, and is easily extensible to other languages. The software is freely available under the LGPL license. The lemmatization is based on the Helsinki Finite-State Transducer Technology …...

AbstractTo facilitate cross-lingual studies, there is an increasing interest in identifying linguistic universals. Recently, a new universal scheme was designed as a part of universal dependency project. In this paper, we map the Arabic tweets dependency treebank (ATDT) to the Universal Dependency (UD) scheme to compare it to other language …...

AbstractStance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance …...

AbstractParaphrases exist on different granularity levels, the most frequently used one being the sentential level. However, we argue that working on the sentential level is not optimal for both machines and humans, and that it would be easier and more efficient to work on sub-sentential levels. To prove this, we …...

AbstractIn this paper, we implement a multilingual Statistical Machine Translation (SMT) system for Arabic-English Translation. Arabic Text can be categorized into standard and dialectal Arabic. These two forms of Arabic differ significantly. Different mono-lingual and multi-lingual hybrid SMT approaches are compared. Mono-lingual systems do always results in better translation accuracy …...

AbstractThis paper is an attempt to exclusively focus on investigating the pivot language technique in which a bridging language is utilized to increase the quality of the Persian-Spanish low-resource Statistical Machine Translation (SMT). In this case, English is used as the bridging language, and the Persian-English SMT is combined with …...

AbstractAutomatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems or data-driven methods can be used. In this paper, we analyze poetic rhythm in English and Spanish. We show that the representations of data …...

AbstractThe last years witnessed an increasing interest in the automatic methods for spotting false translation units in translation memories. This problem presents a great interest to industry as there are many translation memories that contain errors. A closely related line of research deals with identifying sentences that do not align …...

AbstractIn the current context of scientific information overload, text mining tools are of paramount importance for researchers who have to read scientific papers and assess their value. Current citation networks, which link papers by citation relationships (reference and citing paper), are useful to quantitatively understand the value of a piece …...

AbstractWord sense disambiguation is defined as finding the corresponding sense for a target word in a given context, which comprises a major step in text applications. Recently, it has been addressed as an optimization problem. The idea behind is to find a sequence of senses that corresponds to the words …...

Neural Machine Translation (NMT) with an attention mechanism has shown promising results by utilizing word alignments between the source and target sentences. Typically, training of NMT proceeds token-by-token on the target side, where each token is predicted using only a vector representing the current hidden-state, and the previous token. However, …...

Temporality has significantly contributed to various aspects of Natural Language Processing applications. In this paper, we determine the extent to which temporal orientation is preserved when a sentence is translated manually and automatically from the Hindi language to the English language. We show that the manually and automatically identified temporal …...

The main aim of this paper is to investigate automatic quality assessment for spoken language translation (SLT). More precisely, we investigate SLT errors that can be due to transcription (ASR) or to translation (MT) modules. This paper investigates automatic detection of SLT er- rors using a single classifier based on …...

Both statistical (SMT) and neural (NMT) approaches to machine translation (MT) explore large search spaces to produce and score translations. It is however well known that often the top hypothesis as scored by such approaches may not be the best overall translation among those that can be produced. Previous work …...

Both statistical (SMT) and neural (NMT) approaches to machine translation (MT) explore large search spaces to produce and score translations. It is however well known that often the top hypothesis as scored by such approaches may not be the best overall translation among those that can be produced. Previous work …...

Current machine translation systems require large bilingual corpora for training data. With large bilingual corpora, phrase-based and neural-based methods can achieve state-of-the-art performance. Nevertheless, such large bilingual corpora are unavailable for most language pairs called low-resource languages, which causes a bottleneck for the development of ma- chine translation on such …...