4,522 research outputs found
Handling non-compositionality in multilingual CNLs
In this paper, we describe methods for handling multilingual
non-compositional constructions in the framework of GF. We specifically look at
methods to detect and extract non-compositional phrases from parallel texts and
propose methods to handle such constructions in GF grammars. We expect that the
methods to handle non-compositional constructions will enrich CNLs by providing
more flexibility in the design of controlled languages. We look at two specific
use cases of non-compositional constructions: a general-purpose method to
detect and extract multilingual multiword expressions and a procedure to
identify nominal compounds in German. We evaluate our procedure for multiword
expressions by performing a qualitative analysis of the results. For the
experiments on nominal compounds, we incorporate the detected compounds in a
full SMT pipeline and evaluate the impact of our method in machine translation
process.Comment: CNL workshop in COLING 201
Computational Sociolinguistics: A Survey
Language is a social phenomenon and variation is inherent to its social
nature. Recently, there has been a surge of interest within the computational
linguistics (CL) community in the social dimension of language. In this article
we present a survey of the emerging field of "Computational Sociolinguistics"
that reflects this increased interest. We aim to provide a comprehensive
overview of CL research on sociolinguistic themes, featuring topics such as the
relation between language and social identity, language use in social
interaction and multilingual communication. Moreover, we demonstrate the
potential for synergy between the research communities involved, by showing how
the large-scale data-driven methods that are widely used in CL can complement
existing sociolinguistic studies, and how sociolinguistics can inform and
challenge the methods and assumptions employed in CL studies. We hope to convey
the possible benefits of a closer collaboration between the two communities and
conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication:
18th February, 201
Empirical methods for the study of denotation in nominalizations in Spanish
This article deals with deverbal nominalizations in Spanish; concretely, we focus on the denotative distinction between event and result nominalizations. The goals of this work is twofold: first, to detect the most relevant features for this denotative distinction; and, second, to build an automatic classification system of deverbal nominalizations according to their denotation. We have based our study on theoretical hypotheses dealing with this semantic distinction and we have analyzed them empirically by means of Machine Learning techniques which are the basis of the ADN-Classifier. This is the first tool that aims to automatically classify deverbal nominalizations in event, result, or underspecified denotation types in Spanish. The ADN-Classifier has helped us to quantitatively evaluate the validity of our claims regarding deverbal nominalizations. We set up a series of experiments in order to test the ADN-Classifier with different models and in different realistic scenarios depending on the knowledge resources and natural language processors available. The ADN-Classifier achieved good results (87.20% accuracy)
Sentiment Analysis: An Overview from Linguistics
Sentiment analysis is a growing field at the intersection of linguistics and computer science, which attempts to automatically determine the sentiment, or positive/negative opinion, contained in text. Sentiment can be characterized as positive or negative evaluation expressed through language. Common applications of sentiment analysis include the automatic determination of whether a review posted online (of a movie, a book, or a consumer product) is positive or negative towards the item being reviewed. Sentiment analysis is now a common tool in the repertoire of social media analysis carried out by companies, marketers and political analysts. Research on sentiment analysis extracts information from positive and negative words in text, from the context of those words, and the linguistic structure of the text. This brief survey examines in particular the contributions that linguistic knowledge can make to the problem of automatically determining sentiment
Corpus linguistics as digital scholarship : Big data, rich data and uncharted data
This introductory chapter begins by considering how the fields of corpus linguistics, digital linguistics and digital humanities overlap, intertwine and feed off each other when it comes to making use of the increasing variety of resources available for linguistic research today. We then move on to discuss the benefits and challenges of three partly overlapping approaches to the use of digital data sources: (1) increasing data size to create “big data”, (2) supplying multi-faceted co(n)textual information and analyses to produce “rich data”, and (3) adapting existing data sets to new uses by drawing on hitherto “uncharted data”. All of them also call for new digital tools and methodologies that, in Tim Hitchcock’s words, “allow us to think small; at the same time as we are generating tools to imagine big.” We conclude the chapter by briefly describing how the contributions in this volume make use of their various data sources to answer new research questions about language use and to revisit old questions in new ways.Peer reviewe
Detecting machine-translated subtitles in large parallel corpora
Parallel corpora extracted from online repositories of movie and TV subtitles are employed in a wide range of NLP applications, from language modelling to machine translation and dialogue systems. However, the subtitles uploaded in such repositories exhibit varying levels of quality. A particularly difficult problem stems from the fact that a substantial number of these subtitles are not written by human subtitlers but are simply generated through the use of online translation engines. This paper investigates whether these machine-generated subtitles can be detected automatically using a combination of linguistic and extra-linguistic features. We show that a feedforward neural network trained on a small dataset of subtitles can detect machine-generated subtitles with a F1-score of 0.64. Furthermore, applying this detection model on an unlabelled sample of subtitles allows us to provide a statistical estimate for the proportion of subtitles that are machine-translated (or are at least of very low quality) in the full corpus
Coherence in Machine Translation
Coherence ensures individual sentences work together to form a meaningful document. When properly translated, a coherent document in one language should result in a coherent document in another language. In Machine Translation, however, due to reasons of modeling and computational complexity, sentences are pieced together from words or phrases based on short context windows and
with no access to extra-sentential context.
In this thesis I propose ways to automatically assess the coherence of machine translation output. The work is structured around three dimensions: entity-based coherence, coherence as evidenced via syntactic patterns, and coherence as
evidenced via discourse relations.
For the first time, I evaluate existing monolingual coherence models on this new task, identifying issues and challenges that are specific to the machine translation setting. In order to address these issues, I adapted a state-of-the-art syntax
model, which also resulted in improved performance for the monolingual task. The results clearly indicate how much more difficult the new task is than the task of detecting shuffled texts. I proposed a new coherence model, exploring the crosslingual transfer of discourse relations in machine translation. This model is novel in that it measures the correctness of the discourse relation by comparison to the source text rather than to a reference translation. I identified patterns of incoherence common across different language pairs, and created a corpus of machine translated output annotated with coherence errors for evaluation purposes. I then examined
lexical coherence in a multilingual context, as a preliminary study for crosslingual transfer. Finally, I determine how the new and adapted models correlate with human judgements of translation quality and suggest that improvements in general evaluation within machine translation would benefit from having a coherence component that evaluated the translation output with respect to the source text
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