Computational Phraseology light: automatic translation of multiword expressions without translation resources

This paper describes the first phase of a project whose ultimate goal is the implementation of a practical tool to support the work of language learners and translators by automatically identifying multiword expressions (MWEs) and retrieving their translations for any pair of languages. The task of translating multiword expressions is viewed as a two-stage process. The first stage is the extraction of MWEs in each of the languages; the second stage is a matching procedure for the extracted MWEs in each language which proposes the translation equivalents. This project pursues the development of a knowledge-poor approach for any pair of languages which does not depend on translation resources such as dictionaries, translation memories or parallel corpora which can be time consuming to develop or difficult to acquire, being expensive or proprietary. In line with this philosophy, the methodology developed does not rely on any dictionaries or parallel corpora, nor does it use any (bilingual) grammars. The only information comes from comparable corpora, inexpensively compiled. The first proofof- concept stage of this project covers English and Spanish and focuses on a particular subclass of MWEs: verb-noun expressions (collocations) such as take advantage, make sense, prestar atención and tener derecho. The choice of genre was determined by the fact that newswire is a widespread genre and available in different languages. An additional motivation was the fact that the methodology was developed as language independent with the objective of applying it to and testing it for different languages. The ACCURAT toolkit (Pinnis et al. 2012; Skadina et al. 2012; Su and Babych 2012a) was employed to compile automatically the comparable corpora and documents only above a specific threshold were considered for inclusion. More specifically, only pairs of English and Spanish documents with comparability score (cosine similarity) higher 0.45 were extracted. Statistical association measures were employed to quantify the strength of the relationship between two words and to propose that a combination of a verb and a noun above a specific threshold would be a (candidate for) multiword expression. This study focused on and compared four popular and established measures along with frequency: Log-likelihood ratio, T-Score, Log Dice and Salience. This project follows the distributional similarity premise which stipulates that translation equivalents share common words in their contexts and this applies also to multiword expressions. The Vector Space Model is traditionally used to represent words with their co-occurrences and to measure similarity. The vector representation for any word is constructed from the statistics of the occurrences of that word with other specific/context words in a corpus of texts. In this study, the word2vec method (Mikolov et al. 2013) was employed. Mikolov et al.’s method utilises patterns of word co-occurrences within a small window to predict similarities among words. Evaluation results are reported for both extracting MWEs and their automatic translation. A finding of the evaluation worth mentioning is that the size of the comparable corpora is more important for the performance of automatic translation of MWEs than the similarity between them as long as the comparable corpora used are of minimal similarity

Nuevos entornos formativos para la comunicación médico-paciente

El proyecto Tell-me tiene como meta la mejora del acceso a la sanidad en Europa. Para ello propone eliminar barreras multiculturales, como el idioma, con planes de formación lingüística para médicos que trabajen en el extranjero y pacientes que no hablen la lengua del país en el que son atendidos

Mutual terminology extraction using a statistical framework

In this paper, we explore a statistical framework for mutual bilingual terminology extraction. We propose three probabilistic models to assess the proposition that automatic alignment can play an active role in bilingual terminology extraction and translate it into mutual bilingual terminology extraction. The results indicate that such models are valid and can show that mutual bilingual terminology extraction is indeed a viable approach

WLV at SemEval-2018 task 3: Dissecting tweets in search of irony

International Workshop on Semantic Evaluation. WLV at SemEval-2018 Task 3.This paper describes the systems submitted to SemEval 2018 Task 3 “Irony detection in English tweets” for both subtasks A and B. The first system leveraging a combination of sentiment, distributional semantic, and text surface features is ranked third among 44 teams according to the official leaderboard of the subtask A. The second system with slightly different representation of the features ranked ninth in subtask B. We present a method that entails decomposing tweets into separate parts. Searching for contrast within the constituents of a tweet is an integral part of our system. We embrace an extensive definition of contrast which leads to a vast coverage in detecting ironic content.Research Group in Computational Linguistic

Temporal processing of news : annotation of temporal expressions, verbal events and temporal relations

The ability to capture the temporal dimension of a natural language text is essential to many natural language processing applications, such as Question Answering, Automatic Summarisation, and Information Retrieval. Temporal processing is a ¯eld of Computational Linguistics which aims to access this dimension and derive a precise temporal representation of a natural language text by extracting time expressions, events and temporal relations, and then representing them according to a chosen knowledge framework. This thesis focuses on the investigation and understanding of the di®erent ways time is expressed in natural language, on the implementation of a temporal processing system in accordance with the results of this investigation, on the evaluation of the system, and on the extensive analysis of the errors and challenges that appear during system development. The ultimate goal of this research is to develop the ability to automatically annotate temporal expressions, verbal events and temporal relations in a natural language text. Temporal expression annotation involves two stages: temporal expression identi¯cation concerned with determining the textual extent of a temporal expression, and temporal expression normalisation which ¯nds the value that the temporal expression designates and represents it using an annotation standard. The research presented in this thesis approaches these tasks with a knowledge-based methodology that tackles temporal expressions according to their semantic classi¯cation. Several knowledge sources and normalisation models are experimented with to allow an analysis of their impact on system performance. The annotation of events expressed using either ¯nite or non-¯nite verbs is addressed with a method that overcomes the drawback of existing methods v which associate an event with the class that is most frequently assigned to it in a corpus and are limited in coverage by the small number of events present in the corpus. This limitation is overcome in this research by annotating each WordNet verb with an event class that best characterises that verb. This thesis also describes an original methodology for the identi¯cation of temporal relations that hold among events and temporal expressions. The method relies on sentence-level syntactic trees and a propagation of temporal relations between syntactic constituents, by analysing syntactic and lexical properties of the constituents and of the relations between them. The detailed evaluation and error analysis of the methods proposed for solving di®erent temporal processing tasks form an important part of this research. Various corpora widely used by researchers studying di®erent temporal phenomena are employed in the evaluation, thus enabling comparison with state of the art in the ¯eld. The detailed error analysis targeting each temporal processing task helps identify not only problems of the implemented methods, but also reliability problems of the annotated resources, and encourages potential reexaminations of some temporal processing tasks.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Unsupervised relation extraction for e-learning applications

In this modern era many educational institutes and business organisations are adopting the e-Learning approach as it provides an effective method for educating and testing their students and staff. The continuous development in the area of information technology and increasing use of the internet has resulted in a huge global market and rapid growth for e-Learning. Multiple Choice Tests (MCTs) are a popular form of assessment and are quite frequently used by many e-Learning applications as they are well adapted to assessing factual, conceptual and procedural information. In this thesis, we present an alternative to the lengthy and time-consuming activity of developing MCTs by proposing a Natural Language Processing (NLP) based approach that relies on semantic relations extracted using Information Extraction to automatically generate MCTs. Information Extraction (IE) is an NLP field used to recognise the most important entities present in a text, and the relations between those concepts, regardless of their surface realisations. In IE, text is processed at a semantic level that allows the partial representation of the meaning of a sentence to be produced. IE has two major subtasks: Named Entity Recognition (NER) and Relation Extraction (RE). In this work, we present two unsupervised RE approaches (surface-based and dependency-based). The aim of both approaches is to identify the most important semantic relations in a document without assigning explicit labels to them in order to ensure broad coverage, unrestricted to predefined types of relations. In the surface-based approach, we examined different surface pattern types, each implementing different assumptions about the linguistic expression of semantic relations between named entities while in the dependency-based approach we explored how dependency relations based on dependency trees can be helpful in extracting relations between named entities. Our findings indicate that the presented approaches are capable of achieving high precision rates. Our experiments make use of traditional, manually compiled corpora along with similar corpora automatically collected from the Web. We found that an automatically collected web corpus is still unable to ensure the same level of topic relevance as attained in manually compiled traditional corpora. Comparison between the surface-based and the dependency-based approaches revealed that the dependency-based approach performs better. Our research enabled us to automatically generate questions regarding the important concepts present in a domain by relying on unsupervised relation extraction approaches as extracted semantic relations allow us to identify key information in a sentence. The extracted patterns (semantic relations) are then automatically transformed into questions. In the surface-based approach, questions are automatically generated from sentences matched by the extracted surface-based semantic pattern which relies on a certain set of rules. Conversely, in the dependency-based approach questions are automatically generated by traversing the dependency tree of extracted sentence matched by the dependency-based semantic patterns. The MCQ systems produced from these surface-based and dependency-based semantic patterns were extrinsically evaluated by two domain experts in terms of questions and distractors readability, usefulness of semantic relations, relevance, acceptability of questions and distractors and overall MCQ usability. The evaluation results revealed that the MCQ system based on dependency-based semantic relations performed better than the surface-based one. A major outcome of this work is an integrated system for MCQ generation that has been evaluated by potential end users.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Identification of translationese: a machine learning approach

This paper presents a machine learning approach to the study of translationese. The goal is to train a computer system to distinguish between translated and non-translated text, in order to determine the characteristic features that influence the classifiers. Several algorithms reach up to 97.62% success rate on a technical dataset. Moreover, the SVM classifier consistently reports a statistically significant improved accuracy when the learning system benefits from the addition of simplification features to the basic translational classifier system. Therefore, these findings may be considered an argument for the existence of the Simplification Universal

A dynamic programming approach to improving translation memory matching and retrieval using paraphrases

Translation memory tools lack semantic knowledge like paraphrasing when they perform matching and retrieval. As a result, paraphrased segments are often not retrieved. One of the primary reasons for this is the lack of a simple and efficient algorithm to incorporate paraphrasing in the TM matching process. Gupta and Or˘asan [1] proposed an algorithm which incorporates paraphrasing based on greedy approximation and dynamic programming. However, because of greedy approximation, their approach does not make full use of the paraphrases available. In this paper we propose an efficient method for incorporating paraphrasing in matching and retrieval based on dynamic programming only. We tested our approach on English-German, English-Spanish and English-French language pairs and retrieved better results for all three language pairs compared to the earlier approac