Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This article provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to many applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.

A Survey of the Usages of Deep Learning for Natural Language Processing

Each year the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their systems on exactly the same data sets, in order to better compare systems. The tenth CoNLL (CoNLL-X) saw a shared task on Multilingual Dependency Parsing. In this paper, we describe how treebanks for 13 languages were converted into the same dependency format and how parsing performance was measured. We also give an overview of the parsing approaches that participants took and the results that they achieved. Finally, we try to draw general conclusions about multi-lingual parsing: What makes a particular language, treebank or annotation scheme easier or harder to parse and which phenomena are challenging for any dependency parser?

/pdf/conll-x-shared-task-on-multilingual-dependency-parsing-wnslqiaqbu.pdf

CoNLL-X Shared Task on Multilingual Dependency Parsing

The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learning systems on the same data sets. In 2007, as in 2006, the shared task has been devoted to dependency parsing, this year with both a multilingual track and a domain adaptation track. In thispaper, we definethe tasksof the different tracks and describe how the data sets were created from existing treebanks for ten languages. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results.

/pdf/the-conll-2007-shared-task-on-dependency-parsing-3erv3sel00.pdf

The CoNLL 2007 Shared Task on Dependency Parsing

The Languages of Native North America. Marianne Mithun. Cambridge: Cambridge University Press, 1999. 773 pp.

The Languages of Native North America

The Conference on Computational Natural Language Learning is accompanied every year by a shared task whose purpose is to promote natural language processing applications and evaluate them in a standard setting. In 2008 the shared task was dedicated to the joint parsing of syntactic and semantic dependencies. This shared task not only unifies the shared tasks of the previous four years under a unique dependency-based formalism, but also extends them significantly: this year's syntactic dependencies include more information such as named-entity boundaries; the semantic dependencies model roles of both verbal and nominal predicates. In this paper, we define the shared task and describe how the data sets were created. Furthermore, we report and analyze the results and describe the approaches of the participating systems.

/pdf/the-conll-2008-shared-task-on-joint-parsing-of-syntactic-and-21dlf5u1z0.pdf

The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies

Localization of sound sources in robotics: A review

Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neural sequence-to-sequence (seq2seq) models define the state of the art for morphological segmentation in high-resource settings and for (mostly) European languages, we first show that they also obtain competitive performance for Mexican polysynthetic languages in minimal-resource settings. We then propose two novel multi-task training approaches—one with, one without need for external unlabeled resources—, and two corresponding data augmentation methods, improving over the neural baseline for all languages. Finally, we explore cross-lingual transfer as a third way to fortify our neural model and show that we can train one single multi-lingual model for related languages while maintaining comparable or even improved performance, thus reducing the amount of parameters by close to 75%. We provide our morphological segmentation datasets for Mexicanero, Nahuatl, Wixarika and Yorem Nokki for future research.

/pdf/fortification-of-neural-morphological-segmentation-models-2lmzu99qd6.pdf

Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages

Indigenous languages of the American continent are highly diverse. However, they have received little attention from the technological perspective. In this paper, we review the research, the digital resources and the available NLP systems that focus on these languages. We present the main challenges and research questions that arise when distant languages and low-resource scenarios are faced. We would like to encourage NLP research in linguistically rich and diverse areas like the Americas.

Challenges of language technologies for the indigenous languages of the Americas

Probabilistic Finite-State morphological segmenter for Wixarika (huichol) language

The facial expression of angry emotion can be useful to direct the interaction between agents, especially in unclear and cluttered environments. During the presence of an angry face, a process of a...

https://journals.sagepub.com/doi/pdf/10.1177/1729881418817972

Robotics facial expression of anger in collaborative human–robot interaction

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