LDM-PT - A Portuguese Lexicon of Discourse Markers

The Lexicon of Discourse Markers (LDM-PT) provides a set of lexical items in Portuguese that have the function of structuring discourse and ensuring textual cohesion and coherence at intrasentential and inter-sentential levels. Each connective is associated to the set of its rhetorical senses, following the PDTB typology.info:eu-repo/semantics/publishedVersio

A Lexicon of Discourse Markers for Portuguese – LDM-PT

We present LDM-PT, a lexicon of discourse markers for European Portuguese, composed of 252 pairs of discourse marker/rhetorical sense. The lexicon covers conjunctions, prepositions, adverbs, adverbial phrases and alternative lexicalizations with a connective function, as in the PDTB (Prasad et al., 2008; Prasad et al., 2010). For each discourse marker in the lexicon, there is information regarding its type, category, mood and tense restrictions over the sentence it introduces, rhetorical sense, following the PDTB 3.0 sense hierarchy (Webber et al., 2016), as well as a link to an English near-synonym and a corpus example. The lexicon is compiled in a single excel spread sheet that is later converted to an XML scheme compatible with the DiMLex format (Stede, 2002). We give a detailed description of the contents and format of the lexicon, and discuss possible applications of this resource for discourse studies and discourse processing tools for Portuguese.info:eu-repo/semantics/publishedVersio

Using a discourse bank and a lexicon for the automatic identification of discourse connectives

We describe two new resources that have been prepared for European Portuguese and how they are used for discourse parsing: the Portuguese subpart of the TED-MDB corpus, a multilingual corpus of TED Talks that has been annotated in the PDTB style, and the Lexicon of Discourse Markers for Portuguese (LDM-PT). Both lexicon and corpus are used in a preliminary experiment for discourse connective identification in texts. This includes, in many cases, the difficult task of disambiguating between connective and non-connective uses. We annotated the PT-TED-MDB corpus with POS, lemma and syntactic constituency and focus on the 10 most frequent connectives in the corpus. The best approach considers word-form+POS+syntactic annotation and leads to 85% precision.info:eu-repo/semantics/publishedVersio

First International Workshop on Lexical Resources

International audienceLexical resources are one of the main sources of linguistic information for research and applications in Natural Language Processing and related fields. In recent years advances have been achieved in both symbolic aspects of lexical resource development (lexical formalisms, rule-based tools) and statistical techniques for the acquisition and enrichment of lexical resources, both monolingual and multilingual. The latter have allowed for faster development of large-scale morphological, syntactic and/or semantic resources, for widely-used as well as resource-scarce languages. Moreover, the notion of dynamic lexicon is used increasingly for taking into account the fact that the lexicon undergoes a permanent evolution.This workshop aims at sketching a large picture of the state of the art in the domain of lexical resource modeling and development. It is also dedicated to research on the application of lexical resources for improving corpus-based studies and language processing tools, both in NLP and in other language-related fields, such as linguistics, translation studies, and didactics

Signaling coherence relations in text generation: A case study of German temporal discourse markers

This thesis addresses the question of discourse marker choice in automatic (multilingual) text generation (MLG), in particular the issue of signaling temporal coherence relations on the linguistic surface by means of discourse markers such as nachdem, als, bevor . Current text generation systems do not pay attention to the fine-grained differences in meaning (semantic and pragmatic) between similar discourse markers. Yet, choosing the appropriate marker in a given context requires detailed knowledge of the function and form of a wide range of discourse markers, and a generation architecture that integrates discourse marker choice into the overall generation process. This thesis makes contributions to these two distinct areas of research. (1) Linguistic description and representation: The thesis provides a comprehensive analysis of the semantic, pragmatic and syntactic properties of German temporal discourse markers. The results are merged into a functional classification of German temporal conjunctive relations (following the Systemic functional linguistics (SFL) approach to language). This classification is compared to existing accounts for English and Dutch. Further, the thesis addresses the question of the nature of coherence relations and proposes a paradigmatic description of coherence relations along three dimensions (ideation, interpersonal, textual), yielding composite coherence relations. (2) Discourse marker choice in text generation: The thesis proposes a discourse marker lexicon as a generic resource for storing discourse marker meaning and usage, and defines the shape of individual lexicon entries and the global organisation of the lexicon. Sample entries for German and English temporal discourse markers are given. Finally, a computational model for automatic discourse marker choice that exploits the discourse marker lexicon is presente

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

Cross-domain analysis of discourse markers in European Portuguese

This paper presents an analysis of discourse markers in two spontaneous speech corpora for European Portuguese - university lectures and map-task dialogues - and also in a collection of tweets, aiming at contributing to their categorization, scarcely existent for European Portuguese. Our results show that the selection of discourse markers is domain and speaker dependent. We also found that the most frequent discourse markers are similar in all three corpora, despite tweets containing discourse markers not found in the other two corpora. In this multidisciplinary study, comprising both a linguistic perspective and a computational approach, discourse markers are also automatically discriminated from other structural metadata events, namely sentence-like units and disfluencies. Our results show that discourse markers and disfluencies tend to co-occur in the dialogue corpus, but have a complementary distribution in the university lectures. We used three acoustic-prosodic feature sets and machine learning to automatically distinguish between discourse markers, disfluencies and sentence-like units. Our in-domain experiments achieved an accuracy of about 87% in university lectures and 84% in dialogues, in line with our previous results. The eGeMAPS features, commonly used for other paralinguistic tasks, achieved a considerable performance on our data, especially considering the small size of the feature set. Our results suggest that turn-initial discourse markers are usually easier to classify than disfluencies, a result also previously reported in the literature. We conducted a cross-domain evaluation in order to evaluate the robustness of the models across domains. The results achieved are about 11%-12% lower, but we conclude that data from one domain can still be used to classify the same events in the other. Overall, despite the complexity of this task, these are very encouraging state-of-the-art results. Ultimately, using exclusively acoustic-prosodic cues, discourse markers can be fairly discriminated from disfluencies and SUs. In order to better understand the contribution of each feature, we have also reported the impact of the features in both the dialogues and the university lectures. Pitch features are the most relevant ones for the distinction between discourse markers and disfluencies, namely pitch slopes. These features are in line with the wide pitch range of discourse markers, in a continuum from a very compressed pitch range to a very wide one, expressed by total deaccented material or H+L* L* contours, with upstep H tones

Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language