There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

/pdf/deep-contextualized-word-representations-11iek0hsjp.pdf

Deep contextualized word representations

Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models to learn such representations  ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram, words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.

/pdf/enriching-word-vectors-with-subword-information-22sqq6w40j.pdf

Enriching Word Vectors with Subword Information

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Query specification for 3D object retrieval still relies on traditional interaction paradigms. The goal of our study was to identify the most natural methods to describe 3D objects, focusing on verbal and gestural expressions. Our case study uses LEGO R blocks. We started by collecting a corpus involving ten pairs of subjects, in which one participant requests blocks for building a model from another participant. This small corpus suggests that users prefer to describe 3D objects verbally, rarely resorting to gestures, and using them only as complement. The paper describes this corpus, addressing the challenges that such verbal descriptions create for a speech understanding system, namely the long complex verbal descriptions, involving dimensions, shapes, colors, metaphors, and diminutives. The latter connote small size, endearment or insignificance, and are only very common in informal language. In this corpus, they occurred in one out of seven requests. This experiment was the first step of the development of a prototype for searching LEGO R blocks combining speech and stereoscopic 3D. Although the verbal interaction in the first version is limited to relatively simple queries, its combination with immersive visualization allows the user to explore query results in a dataset with virtual blocks. Index Terms: multimodal corpus, 3D objects, voice search

http://www.inesc-id.pt/pt/indicadores/Ficheiros/10550.pdf

Verbal description of LEGO blocks

Second Generation Recogniser

Voice message processing apparatus and method

A Linguateca tem efectivamente funcionado desde a sua criação como um centro de recursos para a língua portuguesa, que se tem revelado desde o início uma importante força congregadora dos investigadores interessados no processamento computacional da língua portuguesa, nomeadamente quanto a troca de experências, a divulgação de informação actualizada, a disponibilização de recursos e de prestação de serviços, a preparação de encontros e workshops, actuando de uma forma totalmente aberta que transmite entusiasmo e confiança tanto aos seus parceiros e formandos como aos próprios clientes. Como uma prova do grande dinamismo e vitalidade da Linguateca será suficiente percorrer a extensa lista de "produção escrita", que teve início apenas em 1999, e que se encontra disponível na página 'online'.

Pareceres sobre Linguateca: Relatório relativo ao período 2000-2003

This paper investigates the capitalization task over Broadcast News speech transcriptions. Most of the capitalization information is provided by two large newspaper corpora, and the spoken language model is produced by retraining the newspaper language models with spoken data. Three different corpora subsets from different time periods are used for evaluation, revealing the importance of available training data in nearby time periods. Results are provided both for manual and automatic transcriptions, showing also the impact of the recognition errors in the capitalization task. Our approach is based on maximum entropy models and uses unlimited vocabulary. The language model produced with this approach can be sorted and then pruned, in order to reduce computational resources, without much impact in the final results.

http://www.inesc-id.pt/pt/indicadores/Ficheiros/4785.pdf

Temporal Issues and Recognition Errors on the Capitalization of Speech Transcriptions

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