Publications

We investigated the influence of prosodic structure and information density on vowel space size. Vowels were measured in five languages from the BonnTempo corpus, French, German, Finnish, Czech, and Polish, each with three female and three male speakers. Speakers read the text at normal, slow, and fast speech rate. The Euclidean distance between vowel space midpoint and formant values for each speaker was used as a measure for vowel distinctiveness. The prosodic model consisted of prominence and boundary. Information density was calculated for each language using the surprisal of the biphone Xn|Xn−1. On average, there is a positive relationship between vowel space expansion and information density. Detailed analysis revealed that this relationship did not hold for Finnish, and was only weak for Polish. When vowel distinctiveness was modeled as a function of prosodic factors and information density in linear mixed effects models (LMM), only prosodic factors were significant in explaining the variance in vowel space expansion. All prosodic factors, except word boundary, showed significant positive results in LMM. Vowels were more distinct in stressed syllables, before a prosodic boundary and at normal and slow speech rate compared to fast speech.

• schulz_etal_sp2016-1     (0.43MB)
• https://www.researchgate.net/publication/303755409_Impact_of_prosodic_structure_and_information_density_on_vowel_space_size

@inproceedings{Schulz/etal:2016a,
title = {Impact of Prosodic Structure and Information Density on Vowel Space Size},
author = {Erika Schulz and Yoon Mi Oh and Bistra Andreeva and Bernd M{\"o}bius},
url = {https://www.researchgate.net/publication/303755409_Impact_of_prosodic_structure_and_information_density_on_vowel_space_size},
year = {2016},
date = {2016},
booktitle = {Proceedings of Speech Prosody},
pages = {350-354},
address = {Boston, MA, USA},
abstract = {We investigated the influence of prosodic structure and information density on vowel space size. Vowels were measured in five languages from the BonnTempo corpus, French, German, Finnish, Czech, and Polish, each with three female and three male speakers. Speakers read the text at normal, slow, and fast speech rate. The Euclidean distance between vowel space midpoint and formant values for each speaker was used as a measure for vowel distinctiveness. The prosodic model consisted of prominence and boundary. Information density was calculated for each language using the surprisal of the biphone Xn|Xn−1. On average, there is a positive relationship between vowel space expansion and information density. Detailed analysis revealed that this relationship did not hold for Finnish, and was only weak for Polish. When vowel distinctiveness was modeled as a function of prosodic factors and information density in linear mixed effects models (LMM), only prosodic factors were significant in explaining the variance in vowel space expansion. All prosodic factors, except word boundary, showed significant positive results in LMM. Vowels were more distinct in stressed syllables, before a prosodic boundary and at normal and slow speech rate compared to fast speech.},
pubstate = {published},
type = {inproceedings}
}

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• https://www.researchgate.net/publication/305684951_Toward_the_use_of_information_density_based_descriptive_features_in_HMM_based_speech_synthesis

@inproceedings{LeMaguer2016SP,
title = {Toward the use of information density based descriptive features in HMM based speech synthesis},
author = {S{\'e}bastien Le Maguer and Bernd M{\"o}bius and Ingmar Steiner},
url = {https://www.researchgate.net/publication/305684951_Toward_the_use_of_information_density_based_descriptive_features_in_HMM_based_speech_synthesis},
year = {2016},
date = {2016},
booktitle = {8th International Conference on Speech Prosody},
pages = {1029-1033},
address = {Boston, MA, USA},
abstract = {

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Durant les dernières décennies, la modélisation acoustique effectuée par les systèmes de synthèse de parole paramétrique a fait l’objet d’une attention particulière. Toutefois, dans la plupart des systèmes connus, l’ensemble des descripteurs linguistiques utilisés pour représenter le texte reste identique. Plus specifiquement, la modélisation de la prosodie reste guidée par des descripteurs de bas niveau comme l’information d’accentuation de la syllabe ou bien l’étiquette grammaticale du mot. Dans cet article, nous proposons d’intégrer des informations basées sur la prédictibilité d’un évènement (la syllabe ou le mot). Plusieurs études indiquent une corrélation forte entre cette mesure, fortement présente dans la linguistique computationnelle, et certaines spécificités lors de la production humaine de la parole. Notre hypothèse est donc que l’ajout de ces descripteurs améliore la modélisation de la prosodie. Cet article se focalise sur une analyse objective de l’apport de ces descripteurs sur la synthèse HMM pour la langue anglaise et française.

• JEP_2016_paper_17     (2.16MB)
• https://aclanthology.org/2016.jeptalnrecital-jep.80

@inproceedings{Lemaguer/etal:2016b,
title = {De l'utilisation de descripteurs issus de la linguistique computationnelle dans le cadre de la synthèse par HMM},
author = {S{\'e}bastien Le Maguer and Bernd M{\"o}bius and Ingmar Steiner and Damien Lolive},
url = {https://aclanthology.org/2016.jeptalnrecital-jep.80},
year = {2016},
date = {2016},
booktitle = {Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP},
pages = {714-722},
publisher = {AFCP - ATALA},
address = {Paris, France},
abstract = {Durant les dernières d{\'e}cennies, la mod{\'e}lisation acoustique effectu{\'e}e par les systèmes de synthèse de parole param{\'e}trique a fait l’objet d’une attention particulière. Toutefois, dans la plupart des systèmes connus, l’ensemble des descripteurs linguistiques utilis{\'e}s pour repr{\'e}senter le texte reste identique. Plus specifiquement, la mod{\'e}lisation de la prosodie reste guid{\'e}e par des descripteurs de bas niveau comme l’information d’accentuation de la syllabe ou bien l’{\'e}tiquette grammaticale du mot. Dans cet article, nous proposons d’int{\'e}grer des informations bas{\'e}es sur la pr{\'e}dictibilit{\'e} d’un {\'e}vènement (la syllabe ou le mot). Plusieurs {\'e}tudes indiquent une corr{\'e}lation forte entre cette mesure, fortement pr{\'e}sente dans la linguistique computationnelle, et certaines sp{\'e}cificit{\'e}s lors de la production humaine de la parole. Notre hypothèse est donc que l’ajout de ces descripteurs am{\'e}liore la mod{\'e}lisation de la prosodie. Cet article se focalise sur une analyse objective de l’apport de ces descripteurs sur la synthèse HMM pour la langue anglaise et française.

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Previous linguistic research on scientific writing has shown that language use in the scientific domain varies considerably in register and style over time. In this paper we investigate the introduction of information theory inspired features to study long term diachronic change on three levels: lexis, part-of-speech and syntax. Our approach is based on distinguishing between sentences from 19th and 20th century scientific abstracts using supervised classification models. To the best of our knowledge, the introduction of information theoretic features to this task is novel. We show that these features outperform more traditional features, such as token or character n-grams, while leading to more compact models. We present a detailed analysis of feature informativeness in order to gain a better understanding of diachronic change on different linguistic levels.

• https://aclanthology.org/C16-1072

@inproceedings{C16-1072,
title = {Modeling Diachronic Change in Scientific Writing with Information Density},
author = {Raphael Rubino and Stefania Degaetano-Ortlieb and Elke Teich and Josef van Genabith},
url = {https://aclanthology.org/C16-1072},
year = {2016},
date = {2016},
booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
pages = {750-761},
publisher = {The COLING 2016 Organizing Committee},
address = {Osaka, Japan},
abstract = {Previous linguistic research on scientific writing has shown that language use in the scientific domain varies considerably in register and style over time. In this paper we investigate the introduction of information theory inspired features to study long term diachronic change on three levels: lexis, part-of-speech and syntax. Our approach is based on distinguishing between sentences from 19th and 20th century scientific abstracts using supervised classification models. To the best of our knowledge, the introduction of information theoretic features to this task is novel. We show that these features outperform more traditional features, such as token or character n-grams, while leading to more compact models. We present a detailed analysis of feature informativeness in order to gain a better understanding of diachronic change on different linguistic levels.},
pubstate = {published},
type = {inproceedings}
}

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This paper introduces information density and machine translation quality estimation inspired features to automatically detect and classify human translated texts. We investigate two settings: discriminating between translations and comparable originally authored texts, and distinguishing two levels of translation professionalism. Our framework is based on delexicalised sentence-level dense feature vector representations combined with a supervised machine learning approach. The results show state-of-the-art performance for mixed-domain translationese detection with information density and quality estimation based features, while results on translation expertise classification are mixed.

• http://aclweb.org/anthology/N16-1110
• https://doi.org/10.18653/v1/N16-1110

@inproceedings{N16-1110,
title = {Information Density and Quality Estimation Features as Translationese Indicators for Human Translation Classification},
author = {Raphael Rubino and Ekaterina Lapshinova-Koltunski and Josef van Genabith},
url = {http://aclweb.org/anthology/N16-1110},
doi = {https://doi.org/10.18653/v1/N16-1110},
year = {2016},
date = {2016},
booktitle = {Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
pages = {960-970},
publisher = {Association for Computational Linguistics},
address = {San Diego, California},
abstract = {This paper introduces information density and machine translation quality estimation inspired features to automatically detect and classify human translated texts. We investigate two settings: discriminating between translations and comparable originally authored texts, and distinguishing two levels of translation professionalism. Our framework is based on delexicalised sentence-level dense feature vector representations combined with a supervised machine learning approach. The results show state-of-the-art performance for mixed-domain translationese detection with information density and quality estimation based features, while results on translation expertise classification are mixed.},
pubstate = {published},
type = {inproceedings}
}

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Significant correlations between words can be observed over long distances, but contemporary language models like N-grams, Skip grams, and recurrent neural network language models (RNNLMs) require a large number of parameters to capture these dependencies, if the models can do so at all. In this paper, we propose the Custom Decay Language Model (CDLM), which captures long range correlations while maintaining sub-linear increase in parameters with vocabulary size. This model has a robust and stable training procedure (unlike RNNLMs), a more powerful modeling scheme than the Skip models, and a customizable representation. In perplexity experiments, CDLMs outperform the Skip models using fewer number of parameters. A CDLM also nominally outperformed a similar-sized RNNLM, meaning that it learned as much as the RNNLM but without recurrence.

• http://dx.doi.org/10.1007/978-3-319-45510-5_39
• https://doi.org/10.1007/978-3-319-45510-5_39

@inbook{Singh2016,
title = {The Custom Decay Language Model for Long Range Dependencies},
author = {Mittul Singh and Clayton Greenberg and Dietrich Klakow},
url = {http://dx.doi.org/10.1007/978-3-319-45510-5_39},
doi = {https://doi.org/10.1007/978-3-319-45510-5_39},
year = {2016},
date = {2016},
booktitle = {Text, Speech, and Dialogue: 19th International Conference, TSD 2016, Brno , Czech Republic, September 12-16, 2016, Proceedings},
isbn = {978-3-319-45510-5},
pages = {343-351},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Significant correlations between words can be observed over long distances, but contemporary language models like N-grams, Skip grams, and recurrent neural network language models (RNNLMs) require a large number of parameters to capture these dependencies, if the models can do so at all. In this paper, we propose the Custom Decay Language Model (CDLM), which captures long range correlations while maintaining sub-linear increase in parameters with vocabulary size. This model has a robust and stable training procedure (unlike RNNLMs), a more powerful modeling scheme than the Skip models, and a customizable representation. In perplexity experiments, CDLMs outperform the Skip models using fewer number of parameters. A CDLM also nominally outperformed a similar-sized RNNLM, meaning that it learned as much as the RNNLM but without recurrence.},
pubstate = {published},
type = {inbook}
}

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Feedforward Neural Network (FNN)-based language models estimate the probability of the next word based on the history of the last N words, whereas Recurrent Neural Networks (RNN) perform the same task based only on the last word and some context information that cycles in the network. This paper presents a novel approach, which bridges the gap between these two categories of networks. In particular, we propose an architecture which takes advantage of the explicit, sequential enumeration of the word history in FNN structure while enhancing each word representation at the projection layer through recurrent context information that evolves in the network. The context integration is performed using an additional word-dependent weight matrix that is also learned during the training. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art feedforward as well as recurrent neural network architectures.

• https://arxiv.org/abs/1703.08068

@article{oualil2016sequential,
title = {Sequential recurrent neural networks for language modeling},
author = {Youssef Oualil and Clayton Greenberg and Mittul Singh and Dietrich Klakow andYoussef Oualil and Singh Mittul},
url = {https://arxiv.org/abs/1703.08068},
year = {2016},
date = {2016},
journal = {Interspeech 2016},
pages = {3509-3513},
abstract = {Feedforward Neural Network (FNN)-based language models estimate the probability of the next word based on the history of the last N words, whereas Recurrent Neural Networks (RNN) perform the same task based only on the last word and some context information that cycles in the network. This paper presents a novel approach, which bridges the gap between these two categories of networks. In particular, we propose an architecture which takes advantage of the explicit, sequential enumeration of the word history in FNN structure while enhancing each word representation at the projection layer through recurrent context information that evolves in the network. The context integration is performed using an additional word-dependent weight matrix that is also learned during the training. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art feedforward as well as recurrent neural network architectures.},
pubstate = {published},
type = {article}
}

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In this paper, we discuss the human thematic fit judgement correlation task in the context of real-valued vector space word representations. Thematic fit is the extent to which an argument fulfils the selectional preference of a verb given a role: for example, how well “cake” fulfils the patient role of “cut”. In recent work, systems have been evaluated on this task by finding the correlations of their output judgements with human-collected judgement data. This task is a representationindependent way of evaluating models that can be applied whenever a system score can be generated, and it is applicable wherever predicate-argument relations are significant to performance in end-user tasks. Significant progress has been made on this cognitive modeling task, leaving considerable space for future, more comprehensive types of evaluation.

• https://www.researchgate.net/publication/306094219_Thematic_fit_evaluation_an_aspect_of_selectional_preferences

@article{Sayeed2016,
title = {Thematic fit evaluation: an aspect of selectional preferences},
author = {Asad Sayeed and Clayton Greenberg and Vera Demberg},
url = {https://www.researchgate.net/publication/306094219_Thematic_fit_evaluation_an_aspect_of_selectional_preferences},
year = {2016},
date = {2016},
journal = {Proceedings of the 1st Workshop on Evaluating Vector Space Representations for NLP},
pages = {99-105},
abstract = {In this paper, we discuss the human thematic fit judgement correlation task in the context of real-valued vector space word representations. Thematic fit is the extent to which an argument fulfils the selectional preference of a verb given a role: for example, how well “cake” fulfils the patient role of “cut”. In recent work, systems have been evaluated on this task by finding the correlations of their output judgements with human-collected judgement data. This task is a representationindependent way of evaluating models that can be applied whenever a system score can be generated, and it is applicable wherever predicate-argument relations are significant to performance in end-user tasks. Significant progress has been made on this cognitive modeling task, leaving considerable space for future, more comprehensive types of evaluation.},
pubstate = {published},
type = {article}
}

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This paper investigates whether Information Theory (IT) in the tradition of Shannon (1948) and in particular the “Uniform Information Density Hypothesis” (UID, see Jager 2010) might contribute to our understanding of a phenomenon called “article omission” (AO) in the literature. To this effect, we trained language models on a corpus of 17 different text types (from prototypically written text types like legal texts to prototypically spoken text types like dialogue) with about 2.000 sentences each and compared the density profiles of minimal pairs. Our results suggest, firstly, that an overtly realized article significantly reduces the surprisal on the following head noun (as was to be expected). It also shows, however, that omitting the article results in a non-uniform distribution (thus contradicting the UID). Since empirically AO seems not to depend on specific lexical items, we also trained our language models on a more abstract level (part of speech). With respect to this level of analysis we were able to show that, again, an overtly realized article significantly reduces the surprisal on the following head noun, but at the same time AO results in a more uniform distribution of information. In the case of AO the UID thus seems to operate on the level of POS rather than on the lexical level.

• 16_konvensproc     (0.34MB)
• https://www.linguistics.rub.de/konvens16/pub/16_konvensproc.pdf

@inproceedings{HorchReich2016,
title = {On “Article Omission” in German and the “Uniform Information Density Hypothesis”},
author = {Ingo Reich and Eva Horch},
editor = {Stefanie Dipper and Friedrich Neubarth and Heike Zinsmeister},
url = {https://www.linguistics.rub.de/konvens16/pub/16_konvensproc.pdf},
year = {2016},
date = {2016},
booktitle = {Proceedings of the 13th Conference on Natural Language Processing (KONVENS 2016)},
pages = {125-127},
address = {Bochum},
abstract = {This paper investigates whether Information Theory (IT) in the tradition of Shannon (1948) and in particular the “Uniform Information Density Hypothesis” (UID, see Jager 2010) might contribute to our understanding of a phenomenon called “article omission” (AO) in the literature. To this effect, we trained language models on a corpus of 17 different text types (from prototypically written text types like legal texts to prototypically spoken text types like dialogue) with about 2.000 sentences each and compared the density profiles of minimal pairs. Our results suggest, firstly, that an overtly realized article significantly reduces the surprisal on the following head noun (as was to be expected). It also shows, however, that omitting the article results in a non-uniform distribution (thus contradicting the UID). Since empirically AO seems not to depend on specific lexical items, we also trained our language models on a more abstract level (part of speech). With respect to this level of analysis we were able to show that, again, an overtly realized article significantly reduces the surprisal on the following head noun, but at the same time AO results in a more uniform distribution of information. In the case of AO the UID thus seems to operate on the level of POS rather than on the lexical level.},
pubstate = {published},
type = {inproceedings}
}

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Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Surface features achieve good performance, but they are not readily applicable to other languages without semantic lexicons. Previous neural models require parses, surface features, or a small label set to work well. Here, we propose neural network models that are based on feedforward and long-short term memory architecture without any surface features. To our surprise, our best configured feedforward architecture outperforms LSTM-based model in most cases despite thorough tuning. Under various fine-grained label sets and a cross-linguistic setting, our feedforward models perform consistently better or at least just as well as systems that require hand-crafted surface features. Our models present the first neural Chinese discourse parser in the style of Chinese Discourse Treebank, showing that our results hold cross-linguistically.

• http://arxiv.org/abs/1606.01990

@article{DBLP:journals/corr/RutherfordDX16,
title = {Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface Features},
author = {Attapol Rutherford and Vera Demberg and Nianwen Xue},
url = {http://arxiv.org/abs/1606.01990},
year = {2016},
date = {2016},
journal = {CoRR},
abstract = {Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Surface features achieve good performance, but they are not readily applicable to other languages without semantic lexicons. Previous neural models require parses, surface features, or a small label set to work well. Here, we propose neural network models that are based on feedforward and long-short term memory architecture without any surface features. To our surprise, our best configured feedforward architecture outperforms LSTM-based model in most cases despite thorough tuning. Under various fine-grained label sets and a cross-linguistic setting, our feedforward models perform consistently better or at least just as well as systems that require hand-crafted surface features. Our models present the first neural Chinese discourse parser in the style of Chinese Discourse Treebank, showing that our results hold cross-linguistically.},
pubstate = {published},
type = {article}
}

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Previous experimental studies on concessive connectives have only looked at their local facilitating or predictive effect on discourse relation comprehension and have often viewed them as a class of discourse markers with similar effects. We look into the effect of two connectives, but and although, for inferring contrastive vs. concessive discourse relations to complement previous experimental work on causal inferences. An offline survey on AMTurk and an online eye-tracking-while-reading experiment are conducted to show that even between these two connectives, which mark the same set of relations, interpretations are biased. The bias is consistent with the distribution of the connective across discourse relations. This suggests that an account of discourse connective meaning based on probability distributions can better account for comprehension data than a classic categorical approach, or an approach where closely related connectives only have a core meaning and the rest of the interpretation comes from the discourse arguments.

• CogSci2016_AsrDemberg     (0.23MB)
• https://www.semanticscholar.org/paper/But-vs.-Although-under-the-microscope-Asr-Demberg/68be3f7ec0d7642f4371d991fc15471416141dfd

@inproceedings{Asr2016b,
title = {But vs. Although under the microscope},
author = {Fatemeh Torabi Asr and Vera Demberg},
url = {https://www.semanticscholar.org/paper/But-vs.-Although-under-the-microscope-Asr-Demberg/68be3f7ec0d7642f4371d991fc15471416141dfd},
year = {2016},
date = {2016},
booktitle = {Proceedings of the 38th Meeting of the Cognitive Science Society},
pages = {366-371},
address = {Philadelphia, Pennsylvania, USA},
abstract = {Previous experimental studies on concessive connectives have only looked at their local facilitating or predictive effect on discourse relation comprehension and have often viewed them as a class of discourse markers with similar effects. We look into the effect of two connectives, but and although, for inferring contrastive vs. concessive discourse relations to complement previous experimental work on causal inferences. An offline survey on AMTurk and an online eye-tracking-while-reading experiment are conducted to show that even between these two connectives, which mark the same set of relations, interpretations are biased. The bias is consistent with the distribution of the connective across discourse relations. This suggests that an account of discourse connective meaning based on probability distributions can better account for comprehension data than a classic categorical approach, or an approach where closely related connectives only have a core meaning and the rest of the interpretation comes from the discourse arguments.},
pubstate = {published},
type = {inproceedings}
}

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In discourse relation annotation, there is currently a variety of different frameworks being used, and most of them have been developed and employed mostly on written data. This raises a number of questions regarding interoperability of discourse relation annotation schemes, as well as regarding differences in discourse annotation for written vs. spoken domains. In this paper, we describe ouron annotating two spoken domains from the SPICE Ireland corpus (telephone conversations and broadcast interviews) according todifferent discourse annotation schemes, PDTB 3.0 and CCR. We show that annotations in the two schemes can largely be mappedone another, and discuss differences in operationalisations of discourse relation schemes which present a challenge to automatic mapping. We also observe systematic differences in the prevalence of implicit discourse relations in spoken data compared to written texts,find that there are also differences in the types of causal relations between the domains. Finally, we find that PDTB 3.0 addresses many shortcomings of PDTB 2.0 wrt. the annotation of spoken discourse, and suggest further extensions. The new corpus has roughly theof the CoNLL 2015 Shared Task test set, and we hence hope that it will be a valuable resource for the evaluation of automatic discourse relation labellers.

• 457_Paper     (0.19MB)
• https://aclanthology.org/L16-1165

@inproceedings{REHBEIN16.457,
title = {Annotating Discourse Relations in Spoken Language: A Comparison of the PDTB and CCR Frameworks},
author = {Ines Rehbein and Merel Scholman and Vera Demberg},
editor = {Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
url = {https://aclanthology.org/L16-1165},
year = {2016},
date = {2016},
booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
isbn = {978-2-9517408-9-1},
pages = {1039-1046},
publisher = {European Language Resources Association (ELRA)},
address = {Portoro{\v{z}, Slovenia},
abstract = {In discourse relation annotation, there is currently a variety of different frameworks being used, and most of them have been developed and employed mostly on written data. This raises a number of questions regarding interoperability of discourse relation annotation schemes, as well as regarding differences in discourse annotation for written vs. spoken domains. In this paper, we describe ouron annotating two spoken domains from the SPICE Ireland corpus (telephone conversations and broadcast interviews) according todifferent discourse annotation schemes, PDTB 3.0 and CCR. We show that annotations in the two schemes can largely be mappedone another, and discuss differences in operationalisations of discourse relation schemes which present a challenge to automatic mapping. We also observe systematic differences in the prevalence of implicit discourse relations in spoken data compared to written texts,find that there are also differences in the types of causal relations between the domains. Finally, we find that PDTB 3.0 addresses many shortcomings of PDTB 2.0 wrt. the annotation of spoken discourse, and suggest further extensions. The new corpus has roughly theof the CoNLL 2015 Shared Task test set, and we hence hope that it will be a valuable resource for the evaluation of automatic discourse relation labellers.},
pubstate = {published},
type = {inproceedings}
}

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The resource contains all texts from the Broadcast interview and Telephone conversation genres from the SPICE-Ireland corpus, annotated with discourse relations according to the PDTB 3.0 and CCR frameworks. Contact person: Merel Scholman

@inproceedings{merel2016,
title = {Disco-SPICE (Spoken conversations from the SPICE-Ireland corpus annotated with discourse relations)},
author = {Ines Rehbein and Merel Scholman and Vera Demberg},
year = {2016},
date = {2016},
booktitle = {Annotating discourse relations in spoken language: A comparison of the PDTB and CCR frameworks. Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 16)},
address = {Portoro{\v{z}, Slovenia},
abstract = {The resource contains all texts from the Broadcast interview and Telephone conversation genres from the SPICE-Ireland corpus, annotated with discourse relations according to the PDTB 3.0 and CCR frameworks. Contact person: Merel Scholman},
pubstate = {published},
type = {inproceedings}
}

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• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4723154/
• https://doi.org/10.1371/journal.pone.0146194

@article{demberg:sayeed:2016:plosone,
title = {The Frequency of Rapid Pupil Dilations as a Measure of Linguistic Processing Difficulty},
author = {Vera Demberg and Asad Sayeed},
editor = {Emmanuel Andreas Stamatakis},
url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4723154/},
doi = {https://doi.org/10.1371/journal.pone.0146194},
year = {2016},
date = {2016},
journal = {PLOS ONE},
volume = {11},
number = {1},
abstract = {

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We present the Royal Society Corpus (RSC) built from the Philosophical Transactions and Proceedings of the Royal Society of London. At present, the corpus contains articles from the first two centuries of the journal (1665-1869) and amounts to around 35 million tokens. The motivation for building the RSC is to investigate the diachronic linguistic development of scientific English. Specifically, we assume that due to specialization, linguistic encodings become more compact over time (Halliday, 1988; Halliday and Martin, 1993), thus creating a specific discourse type characterized by high information density that is functional for expert communication. When building corpora from uncharted material, typically not all relevant meta-data (e.g. author, time, genre) or linguistic data (e.g. sentence/word boundaries, words, parts of speech) is readily available. We present an approach to obtain good quality meta-data and base text data adopting the concept of Agile Software Development.

• https://aclanthology.org/L16-1305

@inproceedings{Kermes2016,
title = {The Royal Society Corpus: From Uncharted Data to Corpus},
author = {Hannah Kermes and Stefania Degaetano-Ortlieb and J{\"o}rg Knappen and Ashraf Khamis and Elke Teich},
url = {https://aclanthology.org/L16-1305},
year = {2016},
date = {2016},
booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)},
pages = {1928-1931},
publisher = {European Language Resources Association (ELRA)},
address = {Portoro{\v{z}, Slovenia},
abstract = {We present the Royal Society Corpus (RSC) built from the Philosophical Transactions and Proceedings of the Royal Society of London. At present, the corpus contains articles from the first two centuries of the journal (1665-1869) and amounts to around 35 million tokens. The motivation for building the RSC is to investigate the diachronic linguistic development of scientific English. Specifically, we assume that due to specialization, linguistic encodings become more compact over time (Halliday, 1988; Halliday and Martin, 1993), thus creating a specific discourse type characterized by high information density that is functional for expert communication. When building corpora from uncharted material, typically not all relevant meta-data (e.g. author, time, genre) or linguistic data (e.g. sentence/word boundaries, words, parts of speech) is readily available. We present an approach to obtain good quality meta-data and base text data adopting the concept of Agile Software Development.},
pubstate = {published},
type = {inproceedings}
}

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Science gradually developed into an established sociocultural domain starting from the mid-17^th century onwards. In this process it became increasingly specialized and diversified. Here, we investigate a particular aspect of specialization on the basis of probabilistic topic models. As a corpus we use the Royal Society Corpus (Khamis et al. 2015), which covers the period from 1665 to 1869 and contains 9015 documents.

• https://www.semanticscholar.org/paper/Topical-Diversification-Over-Time-In-The-Royal-Fankhauser-Knappen/7f7dce0d0b8209d0c841c8da031614fccb97a787

@inproceedings{Fankhauser2016,
title = {Topical Diversification over Time in the Royal Society Corpus},
author = {Peter Fankhauser and J{\"o}rg Knappen and Elke Teich},
url = {https://www.semanticscholar.org/paper/Topical-Diversification-Over-Time-In-The-Royal-Fankhauser-Knappen/7f7dce0d0b8209d0c841c8da031614fccb97a787},
year = {2016},
date = {2016},
booktitle = {Proceedings of Digital Humanities (DH'16)},
address = {Krakow, Poland},
abstract = {Science gradually developed into an established sociocultural domain starting from the mid-17^th century onwards. In this process it became increasingly specialized and diversified. Here, we investigate a particular aspect of specialization on the basis of probabilistic topic models. As a corpus we use the Royal Society Corpus (Khamis et al. 2015), which covers the period from 1665 to 1869 and contains 9015 documents.},
pubstate = {published},
type = {inproceedings}
}

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• https://www.researchgate.net/publication/331648262_The_Royal_Society_Corpus_Towards_a_high-quality_corpus_for_studying_diachronic_variation_in_scientific_writing

@inproceedings{Kermes2016a,
title = {The Royal Society Corpus. Towards a high-quality resource for studying diachronic variation in scientific writing},
author = {Hannah Kermes and J{\"o}rg Knappen and Ashraf Khamis and Stefania Degaetano-Ortlieb and Elke Teich},
url = {https://www.researchgate.net/publication/331648262_The_Royal_Society_Corpus_Towards_a_high-quality_corpus_for_studying_diachronic_variation_in_scientific_writing},
year = {2016},
date = {2016},
booktitle = {Proceedings of Digital Humanities (DH'16)},
address = {Krakow, Poland},
abstract = {

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We present a new approach for modeling diachronic linguistic change in grammatical usage. We illustrate the approach on English scientific writing in Late Modern English, focusing on grammatical patterns that are potentially indicative of shifts in register, genre and/or style. Commonly, diachronic change is characterized by the relative frequency of typical linguistic features over time. However, to fully capture changing linguistic usage, feature productivity needs to be taken into account as well. We introduce a data-driven approach for systematically detecting typical features and assessing their productivity over time, using information-theoretic
measures of entropy and surprisal.

• https://aclanthology.org/W16-2121
• https://doi.org/10.18653/v1/W16-2121

@inproceedings{Degaetano-Ortlieb2016a,
title = {Information-based modeling of diachronic linguistic change: from typicality to productivity},
author = {Stefania Degaetano-Ortlieb and Elke Teich},
url = {https://aclanthology.org/W16-2121},
doi = {https://doi.org/10.18653/v1/W16-2121},
year = {2016},
date = {2016},
booktitle = {Proceedings of Language Technologies for the Socio-Economic Sciences and Humanities (LATECH'16), Association for Computational Linguistics (ACL)},
pages = {165-173},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
abstract = {We present a new approach for modeling diachronic linguistic change in grammatical usage. We illustrate the approach on English scientific writing in Late Modern English, focusing on grammatical patterns that are potentially indicative of shifts in register, genre and/or style. Commonly, diachronic change is characterized by the relative frequency of typical linguistic features over time. However, to fully capture changing linguistic usage, feature productivity needs to be taken into account as well. We introduce a data-driven approach for systematically detecting typical features and assessing their productivity over time, using information-theoretic measures of entropy and surprisal.},
pubstate = {published},
type = {inproceedings}
}

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• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302025/

@inproceedings{Ankener2016,
title = {The influence of visual context on predictions in sentence processing: Evidence from ICA},
author = {Maria Staudte},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302025/},
year = {2016},
date = {2016},
booktitle = {Proceedings at the Language and Perception International Conference},
address = {Trondheim, Norwegen},
abstract = {

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• ESLP2016_book_of_abstracts_2     (10.01MB)

@inproceedings{sekicki2016b,
title = {Cost and Gains of Using Visual Context for Referent Prediction},
author = {Maria Staudte},
year = {2016},
date = {2016-10-18},
booktitle = {Proceedings of the 9th Embodied and Situated Language Processing Conference (ESLP)},
address = {Pucón},
pubstate = {published},
type = {inproceedings}
}

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