scholarly journals Identifying communicative functions in discourse with content types

2021 ◽  
Author(s):  
Tommaso Caselli ◽  
Rachele Sprugnoli ◽  
Giovanni Moretti
Keyword(s):  
Relevant Information ◽  
Annotation Scheme ◽  
Fine Grained ◽  
Single Sentence ◽  
Communicative Functions ◽  
Illocutionary Acts ◽  
Level Of Analysis ◽  
Empirical Framework ◽  
Oriented Approach

AbstractTexts are not monolithic entities but rather coherent collections of micro illocutionary acts which help to convey a unitary message of content and purpose. Identifying such text segments is challenging because they require a fine-grained level of analysis even within a single sentence. At the same time, accessing them facilitates the analysis of the communicative functions of a text as well as the identification of relevant information. We propose an empirical framework for modelling micro illocutionary acts at clause level, that we call content types, grounded on linguistic theories of text types, in particular on the framework proposed by Werlich in 1976. We make available a newly annotated corpus of 279 documents (for a total of more than 180,000 tokens) belonging to different genres and temporal periods, based on a dedicated annotation scheme. We obtain an average Cohen’s kappa of 0.89 at token level. We achieve an average F1 score of 74.99% on the automatic classification of content types using a bi-LSTM model. Similar results are obtained on contemporary and historical documents, while performances on genres are more varied. This work promotes a discourse-oriented approach to information extraction and cross-fertilisation across disciplines through a computationally-aided linguistic analysis.

Argument structure and implicational constructions at the crossroads

2016 ◽  
Vol 14 (2) ◽  
pp. 474-497
Author(s):  
María Sandra Peña Cervel
Keyword(s):  
Argument Structure ◽  
Prepositional Phrase ◽  
Cognitive Mechanisms ◽  
Verb Classes ◽  
Fine Grained ◽  
Communicative Functions ◽  
Corpus Data ◽  
State Of Affairs ◽  
High Level

On the basis of corpus data and in consonance with cognitively-oriented constructionist approaches to language, mainly the work by Goldberg (1995, 2006) and the developments in Ruiz de Mendoza and Mairal (2008, 2011), we offer a fine-grained analysis of some specific instantiations of the resultative pattern based on the prepositional phrase to death. The analysis starts off from the classification of verb classes made by Levin (1993). The verbs in some of these classes are readily available for fusion into the resultative configuration. Others call for reconstrual in terms of high-level metaphor and/or metonymy before they can conform to the requirements of the pattern mentioned above. Thus, we focus on the way in which the resultative pattern overwrites the properties of some lexical groups of verbs through the licensing activity of such cognitive mechanisms as high-level metaphor and metonymy. Additionally, the prepositional phrase to death is shown to perform two key communicative functions from among those put forward by Boas (2003): placing emphasis on an end point or rendering a vague point clear. Finally, the paper examines the hyperbolic load of the PP to death in some contexts where this PP is seen as converting an argument-structure construction into an implicational one conveying the speaker’s (usually negative) reaction to a given state of affairs.

scholarly journals Improving Topic Coherence Using Entity Extraction Denoising

2018 ◽  
Vol 110 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Ronald Cardenas ◽  
Kevin Bello ◽  
Alberto Coronado ◽  
Elizabeth Villota
Keyword(s):  
Topic Modeling ◽  
Human Perception ◽  
Relevant Information ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Fine Grained ◽  
Job Advertisement ◽  
Coherence Score ◽  
Probabilistic Topic Modeling ◽  
Promising Solution

Abstract Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method and the evaluation of this model is an interesting problem on its own. Topic interpretability measures have been developed in recent years as a more natural option for topic quality evaluation, emulating human perception of coherence with word sets correlation scores. In this paper, we show experimental evidence of the improvement of topic coherence score by restricting the training corpus to that of relevant information in the document obtained by Entity Recognition. We experiment with job advertisement data and find that with this approach topic models improve interpretability in about 40 percentage points on average. Our analysis reveals as well that using the extracted text chunks, some redundant topics are joined while others are split into more skill-specific topics. Fine-grained topics observed in models using the whole text are preserved.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals Datives with psych nouns and adjectives in Basque

2020 ◽  
Vol 54 (3) ◽  
pp. 647-696
Author(s):  
Beatriz Fernández ◽  
Fernando Zúñiga ◽  
Ane Berro
Keyword(s):  
Natural Language ◽  
Linguistic Theory ◽  
Psychological State ◽  
Formal Expression ◽  
Fine Grained ◽  
Psych Verbs ◽  
Other Regarding ◽  
Psychological Verbs

Abstract This paper explores the formal expression of two Basque dative argument types in combination with psych nouns and adjectives, in intransitive and transitive clauses: (i) those that express the experiencer, and (ii) those that express the stimulus of the psychological state denoted by the psych noun and adjective. In the intransitive structure involving a dative experiencer (DatExpIS), the stimulus is in the absolutive case, and the intransitive copula izan ‘be’ shows both dative and absolutive agreement. This construction basically corresponds to those built upon the piacere type of psychological verbs typified in (Belletti, Adriana & Luigi Rizzi. 1988. Psych-verbs and θ-theory. Natural Language and Linguistic Theory 6. 291–352) three-way classification of Italian psych verbs. In the intransitive structure involving a dative stimulus (DatStimIS), the experiencer is marked by absolutive case, and the same intransitive copula shows both absolutive and dative agreement (with the latter corresponding to the dative stimulus and not to the experiencer). We show that the behavior of the dative argument in the two constructions is just the opposite of each other regarding a number of morphosyntactic tests, including agreement, constituency, hierarchy and selection. Additionally, we explore two parallel transitive constructions that involve either a dative experiencer and an ergative stimulus (DatExpTS) or a dative stimulus and an ergative experiencer (DatStimTS), which employ the transitive copula *edun ‘have’. Considering these configurations, we propose an extended and more fine-grained typology of psych predicates.

scholarly journals Discovery of MicroRNAs and Their Target Genes Related to Drought in Paulownia “Yuza 1” by High-Throughput Sequencing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Minjie Deng ◽  
Yabing Cao ◽  
Zhenli Zhao ◽  
Lu Yang ◽  
Yanfang Zhang ◽  
...  
Keyword(s):  
Drought Stress ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
Plant Hormone ◽  
Target Genes ◽  
Reference Data ◽  
Relevant Information ◽  
Putative Target

Understanding the role of miRNAs in regulating the molecular mechanisms responsive to drought stress was studied in Paulownia “yuza 1.” Two small RNA libraries and two degradome libraries were, respectively, constructed and sequenced in order to detect miRNAs and their target genes associated with drought stress. A total of 107 miRNAs and 42 putative target genes were identified in this study. Among them, 77 miRNAs were differentially expressed between drought-treated Paulownia “yuza 1” and the control (60 downregulated and 17 upregulated). The predicted target genes were annotated using the GO, KEGG, and Nr databases. According to the functional classification of the target genes, Paulownia “yuza 1” may respond to drought stress via plant hormone signal transduction, photosynthesis, and osmotic adjustment. Furthermore, the expression levels of seven miRNAs (ptf-miR157b, ptf-miR159b, ptf-miR398a, ptf-miR9726a, ptf-M2153, ptf-M2218, and ptf-M24a) and their corresponding target genes were validated by quantitative real-time PCR. The results provide relevant information for understanding the molecular mechanism of Paulownia resistance to drought and reference data for researching drought resistance of other trees.

Change Taxonomy: A Fine-Grained Classification of Software Change

2018 ◽  
Vol 20 (4) ◽  
pp. 28-36 ◽  
Author(s):  
Mohamed Elkholy ◽  
Ahmed Elfatatry
Keyword(s):  
Fine Grained ◽  
Software Change

scholarly journals Experimental analysis on the optimal excitation wavelength for fine-grained identification of refined oil pollutants on water surface based on laser-induced fluorescence

2021 ◽  
Author(s):  
Ming Xie ◽  
Yunpeng Jia ◽  
Ying Li ◽  
Xiaohua Cai ◽  
Kai Cao
Keyword(s):  
Oil Spill ◽  
Theoretical Basis ◽  
Water Surface ◽  
Laser Induced Fluorescence ◽  
Excitation Wavelength ◽  
Refined Oil ◽  
Fine Grained ◽  
Optimal Excitation ◽  
Oil Spill Identification

Abstract Laser-induced fluorescence (LIF) is an effective, all-weather oil spill identification method that has been widely applied for oil spill monitoring. However, the distinguishability on oil types is seldom considered while selecting excitation wavelength. This study is intended to find the optimal excitation wavelength for fine-grained classification of refined oil pollutants using LIF by comparing the distinguishability of fluorometric spectra under various excitation wavelengths on some typical types of refined-oil samples. The results show that the fluorometric spectra of oil samples significantly vary under different excitation wavelengths, and the four types of oil applied in this study are most likely to be distinguished under the excitation wavelengths of 395 nm and 420 nm. This study is expected to improve the ability of oil types identification using LIF method without increasing time or other cost, and also provides theoretical basis for the development of portable LIF devices for oil spill identification.

scholarly journals Densely Supervised Hierarchical Policy-Value Network for Image Paragraph Generation

2019 ◽  
Author(s):  
Siying Wu ◽  
Zheng-Jun Zha ◽  
Zilei Wang ◽  
Houqiang Li ◽  
Feng Wu
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Cross Entropy ◽  
Image Captioning ◽  
Value Network ◽  
Entropy Loss ◽  
Fine Grained ◽  
Performance Improvements ◽  
Single Sentence ◽  
Multiple State

Image paragraph generation aims to describe an image with a paragraph in natural language. Compared to image captioning with a single sentence, paragraph generation provides more expressive and fine-grained description for storytelling. Existing approaches mainly optimize paragraph generator towards minimizing word-wise cross entropy loss, which neglects linguistic hierarchy of paragraph and results in ``sparse" supervision for generator learning. In this paper, we propose a novel Densely Supervised Hierarchical Policy-Value (DHPV) network for effective paragraph generation. We design new hierarchical supervisions consisting of hierarchical rewards and values at both sentence and word levels. The joint exploration of hierarchical rewards and values provides dense supervision cues for learning effective paragraph generator. We propose a new hierarchical policy-value architecture which exploits compositionality at token-to-token and sentence-to-sentence levels simultaneously and can preserve the semantic and syntactic constituent integrity. Extensive experiments on the Stanford image-paragraph benchmark have demonstrated the effectiveness of the proposed DHPV approach with performance improvements over multiple state-of-the-art methods.

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