Reviewerly/exHarmony huggingface dataset:
Taxonomy-Based Feature Extraction for Document Classification, Clustering and Semantic Analysis

Extracting meaningful features from documents can prove critical for a variety of tasks such as classification, clustering and semantic analysis. However, traditional approaches to document feature extraction mainly rely on first-order word statistics that are very high dimensional and do not capture well the semantic of the documents. For this reason, in this paper we present a novel approach that extracts document features based on a combination of a constructed word taxonomy and a word embedding in vector space. The feature extraction consists of three main steps: first, a word embedding technique is used to map all the words in the vocabulary onto a vector space. Second, the words in the vocabulary are organised into a hierarchy of clusters (word clusters) by using k-means hierarchically. Lastly, the individual documents are projected onto the word clusters based on a predefined set of keywords, leading to a compact representation as a mixture of keywords. The extracted features can be used for a number of tasks including document classification and clustering as well as semantic analysis of the documents generated by specific individuals over time. For the experiments, we have employed a dataset of transcripts of phone calls between claim managers and clients collected by the Transport Accident Commission of the Victorian Government. The experimental results show that the proposed approach has been capable of achieving comparable or higher accuracy than conventional feature extraction approaches and with a much more compact representation.
Acquisition of Domain-Specific Senses and Its Extrinsic Evaluation Through Text Categorization

This paper focuses on domain-specific senses and proposes a method for detecting predominant sense depending on each domain. We applied a simple Markov Random Walk (MRW) model to rank senses for each domain. It decides the importance of a vertex (senses) within a graph by using the similarity of senses. The similarity of senses is obtained by using distributed representations of words from gloss texts in the thesaurus. It captures large semantic context and thus does not require manual annotation of sense-tagged data. In order to evaluate the method, we applied the results of domain-specific senses to text categorization. The performance achieved in our test set WordNet3.1 and the Reuters corpus demonstrates applicability for the text categorization task.
Spectral Text Similarity Measures

Estimating semantic similarity between texts is of vital importance in many areas of natural language processing like information retrieval, question answering, text reuse, or plagiarism detection. Prevalent semantic similarity estimates based on word embeddings are noise sensitive. Thus, small individual term similarities can have in aggregate a considerable influence on the total estimation value. In contrast, the methods proposed here exploit the spectrum of the product of embedding matrices, which leads to increased robustness when compared with conventional methods. We apply these estimate on two tasks, which are the assignment of people to the best matching marketing target group and finding the correct match between sentences belonging to two independent translations of the same novel. The evaluation revealed that our proposed method based on the spectral norm could increase the accuracy compared to several baseline methods in both scenarios.
Sentiment Analysis of Influential Messages for Political Election Forecasting

In this paper, we explore the use of sentiment analysis of influential messages on social media to improve political election forecasting. While social media users are not necessarily representative of the overall electors, bias correction of users messages is critical for producing a reliable forecast. The observation motivates our work is that people on social media consult the messages of each other before taking a decision, this means that social media users influence each other. We first built a classifier to detect politically influential messages based on different aspects (messages content, time, sentiment, and emotion). Then, we predicted electoral candidates votes using sentiment degree of influential messages. We applied our proposed model to the 2016 United States presidential election. We conducted experiments at different intervals of times. Results show that our approach achieves better performance than both off-line polling and classical approaches.
Opinion Spam Detection with Attention-Based LSTM Networks

Today, online reviews have a great influence on consumers’ purchasing decisions. As a result, spam attacks, consisting of the malicious inclusion of fake online reviews, can be detrimental to both customers as well as organizations. Several methods have been proposed to automatically detect fake opinions; however, the majority of these methods focus on feature learning techniques based on a large number of handcrafted features. Deep learning and attention mechanisms have recently been shown to improve the performance of many classification tasks as they enable the model to focus on the most the important features. This paper describes our approach to apply LSTM and attention-based mechanisms for detecting deceptive reviews. Experiments with the Three-domain data set [15] show that a BiLSTM model coupled with Multi-Headed Self Attention improves the F-measure from 81.49% to 87.59% in detecting fake reviews.
Using Cognitive Learning Method to Analyze Aggression in Social Media Text

Aggression and hate speech is a rising concern in social media platforms. It is drawing significant attention in the research community who are investigating different methods to detect such content. Aggression, which can be expressed in many forms, is able to leave victims devastated and often scar them for life. Families and social media users prefer a safer platform to interact with each other. Which is why detection and prevention of aggression and hatred over internet is a must. In this paper we extract different features from our social media data and perform supervised learning methods to understand which model produces the best results. We also analyze the features to understand if there is any pattern involved in the features that associates to aggression in social media data. We used state-of-the-art cognitive feature to gain better insight in our dataset. We also employed ngrams sentiment and Part of speech features as a standard model to identify other hate speech and aggression in text. Our model was able to identify texts that contain aggression with an f-score of 0.67.
EAGLE: An Enhanced Attention-Based Strategy by Generating Answers from Learning Questions to a Remote Sensing Image

Image understanding is an essential research issue for many applications, such as text-to-image retrieval, Visual Question Answering, visual dialog and so forth. In all these applications, how to comprehend images through queries has been a challenge. Most studies concentrate on general images and have obtained desired results, but not for some specific real applications, e.g., remote sensing. To tackle this issue, in this paper, we propose an enhanced attention-based approach, entitled EAGLE, which seamlessly integrates the property of aerial images and NLP. In addition, we contribute the first large-scale remote sensing question answering corpus ( https://github.com/rsqa2018/corpus ). Extensive experiments conducted on real data demonstrate that EAGLE outperforms the-state-of-the-art approaches.
Fusing Phonetic Features and Chinese Character Representation for Sentiment Analysis

The Chinese pronunciation system offers two characteristics that distinguish it from other languages: deep phonemic orthography and intonation variations. We are the first to argue that these two important properties can play a major role in Chinese sentiment analysis. Hence, we learn phonetic features of Chinese characters and fuse them with their textual and visual features in order to mimic the way humans read and understand Chinese text. Experimental results on five different Chinese sentiment analysis datasets show that the inclusion of phonetic features significantly and consistently improves the performance of textual and visual representations.
A Hybrid Generative/Discriminative Model for Rapid Prototyping of Domain-Specific Named Entity Recognition

We propose PYHSCRF, a novel tagger for domain-specific named entity recognition that only requires a few seed terms, in addition to unannotated corpora, and thus permits the iterative and incremental design of named entity (NE) classes for new domains. The proposed model is a hybrid of a generative model named PYHSMM and a semi-Markov CRF-based discriminative model, which play complementary roles in generalizing seed terms and in distinguishing between NE chunks and non-NE words. It also allows a smooth transition to full-scale annotation because the discriminative model makes effective use of annotated data when available. Experiments involving two languages and three domains demonstrate that the proposed method outperforms baselines.
Multi-task Learning for Detecting Stance in Tweets

Detecting stance of online posts is a crucial task to understand online content and trends. Existing approaches augment models with complex linguistic features, target-dependent properties, or increase complexity with attention-based modules or pipeline-based architectures. In this work, we propose a simpler multi-task learning framework with auxiliary tasks of subjectivity and sentiment classification. We also analyze the effect of regularization against inconsistent outputs. Our simple model achieves competitive performance with the state of the art in micro-F1 metric and surpasses existing approaches in macro-F1 metric across targets. We are able to show that multi-tasking with a simple architecture is indeed useful for the task of stance classification.
HanaNLG: A Flexible Hybrid Approach for Natural Language Generation

Nowadays, with advances in digital technologies, interaction between computers and humans is essential. In this regard, the area of Natural Language Generation (NLG) can provide techniques capable of facilitating and improving this type of interaction. However, the existing approaches to this field are usually developed ad-hoc for specific tasks, purposes and domains, which hinders the advancement of flexible and adaptable multi-domain NLG systems. Under these premises, the objective of this paper is to present HanaNLG, a hybrid generic NLG approach, focused on the surface realisation stage. HanaNLG combines statistic and knowledge-based techniques and is able to generate text independently of the domain. In particular, this is done by exploiting language models in conjunction with semantic knowledge, providing flexibility to the whole generation process, thus, minimising the high cost associated with the development of common elements involved in NLG, such as grammars. Therefore, taking into account this joint perspective, our approach contributes to advancing the NLG field by providing greater flexibility when it comes to (i) producing text for different domains, and (ii) increasing the variety of vocabulary to appear in the generated text. In order to assess the effectiveness of HanaNLG, it was tested in two domains: (i) NLG for assistive technologies and, (ii) NLG for creating opinionated sentences. The positive results obtained (almost the 99% of the generated sentences for both domains are original and well constructed) show that our approach is capable of generating text for different domains. More importantly, the combination of language models with semantic knowledge enhances the quality of the generated text, thereby improving the results obtained compared to other methods that only rely on statistical methods.
Semantic Roles in VerbNet and FrameNet: Statistical Analysis and Evaluation

Semantic role theory is a widely used approach for verb representation. Yet, there are multiple indications that semantic role paradigm is necessary but not sufficient to cover all elements of verb structure. We conducted a statistical analysis of semantic role representation in VerbNet and FrameNet to provide empirical evidence of insufficiency. The consequence of that is a hybrid role-scalar approach.
Automatic Extraction of Relevant Keyphrases for the Study of Issue Competition

The use of keyphrases is a common oratory technique in political discourse, and politicians often guide their statements by recurrently making use of keyphrases. We propose a statistical method for extracting keyphrases at document level, combining simple heuristic rules. We show that our approach can compete with state-of-the-art systems. The method is particularly useful for the study of policy preferences and issue competition, which relies primarily on the analysis of political statements contained in party manifestos and speeches. As a case study, we show an analysis of Portuguese parliamentary debates. We extract the most used keyphrases from each parliamentary group speech collection to detect political issue emphasis. We additionally show how keyphrase clouds can be used as visualization aids to summarize the main addressed political issues.
Proposal of a simple procedure to derive a more FAIR open data archive than a spreadsheet or a set of CSV files

NuoroForestrySchool (a study center of the Department of Agriculture, University of Sassari, Italy) has developed and published a &amp;#8216;data documentation procedure&amp;#8217; (link to NFS-DDP) enabling the improvement of the dataset FAIRness that any data collector wishes to share as open data.&amp;#160;Datasets are frequently shared as spreadsheet files. While this tool is very handy in data preparation and preliminary analysis, its structure and composition are not very effective for storing and sharing consolidated data, unless data structures are extremely simple.&amp;#160;NFS-DDP takes in input a spreadsheet in which data are organized as relational tables, one per sheet, while four additional sheets contain metadata standardized according to the Dublin Core specifications. The procedure outputs an SQLite relational database (including data and metadata) and a pdf-file documenting the database structure and contents.&amp;#160;A first example application of the proposed procedure was shared by Giadrossich et al. (2022) on the PANGEA repository, concerning experimental data of erosion in forest soil measured during artificial rainfall. The zip-archive that can be downloaded contains the experiment data and metadata processed by NFS-DDP. At the following link is available a test document where basic statistics are computed to show how NFS-DDProcedure facilitates the understanding and correct processing of the shared dataset.&amp;#160;The NFS-DataDocumentationProcedure provides a simple solution for organizing and archiving data aiming to i) achieve a more FAIR archive, ii) exploit data consistency and comprehensibility of semantic connections in the relational database, ii) produce a report documenting the collection and organization of data, providing an effective and concise overview of the whole with all details at hand.Giadrossich, F., Murgia, I., Scotti, R. (2022). Experiment of water runoff and soil erosion with and without forest canopy coverage under intense artificial rainfall. PANGAEA. DOI:10.1594/PANGAEA.94345
Khalat al-Saharij - An Iron Age Small Site and Long Anthropogenic Effect on the Soil

Ancient anthropogenic long-term effects on soil chemical composition is a well-known phenomenon in large archaeological sites. In the current presentation, this effect will be shown in Khalat al-Saharij, a small site located in central Israel, in the footslope of the main highlands of the country. The site served as a farmhouse during the Neo Assyrian rule and was dated to the second half of the 8th century BCE.Archaeological excavations of the site revealed a building that included two strips of rooms built around a square courtyard, a rock-hewn water reservoir, and agricultural facilities scattered in the area east and west of the building, including agricultural terraces.The main aim of the current research was to find an anthropogenic signature in the sediments of the site, and in the sediments of the surrounding fields.&amp;#160;POSL, PXRF and XRF methods were applied in order to achieve this aim.The results showed that a significant anthropogenic signature was recorded on the sediments of the building and its square courtyard. This is reflected in the chemical composition that was enriched by phosphorus (P) and calcium (Ca), and by the mixed pOSL signal values. In the anthropogenic fields, the chemical signature is typical of natural soil with a high amount of Iron (Fe) and Manganese(Mn) which reflect high water availability.&amp;#160;&amp;#160;It is interesting that although the site is small in size and was inhabited for a short period of time, the human imprint has remained hundreds of years after its abandonment.
Making the Case for Reconciled Δ47 Calibrations Using Omnivariant Generalized Least-Squares Regression

For fundamental thermodynamic reasons, 13C-18O bonds in carbonate minerals formed under isotopic equilibrium conditions are more abundant than predicted for a random distribution of isotopes, yielding positive &amp;#916;47 clumped-isotope signatures which decrease as a function of formation temperature [1]. Although most Earth-surface carbonates are unlikely to achieve complete isotopic equilibrium, &amp;#916;47 values of many biogenic and abiotic calcites formed under very different crystallization conditions (and with irreconcilable water-calcite oxygen-18 fractionation laws) appear to follow indistinguishable temperature calibrations, as independently documented by various groups over the years [e.g., 2-4]. That is not to say that all groups agree on a single calibration linking &amp;#916;47 and temperature, and a recent comparison of 14 reprocessed calibration studies still found evidence for statistically significant inter-laboratory discrepancies [3]. Rigorous statistical tests aiming to prove or disprove consistency between &amp;#916;47 calibrations are particularly challenging because of potentially large and non-independent analytical errors associated with standardization procedures [5], and even in some cases by large correlations in the uncertainties of estimated formation temperatures, making classical least-squares regression approaches ill suited to model these calibration data sets.Here I propose a new formulation for least-squares regression of data with an arbitrarily complex covariance structure linking all predictor and response observations, generally applicable to all sorts of geochemical data. I use this &amp;#8220;Omnivariant Generalized Least-Squares&amp;#8221; (OGLS) approach to compare 7 published &amp;#916;47 calibration data sets which have been (re)processed according to the newly established InterCarb Carbon Dioxide Equilibrium Scale (I-CDES), supposedly allowing robust comparisons between &amp;#916;47 measurements across laboratories [6]. None of these reprocessed calibration data sets are found to deviate significantly from a single, unified regression line, with an overall reduced chi-squared statistic (adjusted for data covariance according to OGLS) of 0.8 consistent with slightly overestimated uncertainties on temperature constraints. This finding marks another milestone in the 17-year-long progress of &amp;#916;47 thermometry, which has now solved most of the methodological challenges standing in the way of its widespread application to many scientific issues.&amp;#160;In short: carbonate clumped-isotope thermometry is all grown up now.[1] Schauble et al. (2006) 10.1016/j.gca.2006.02.011[2] Kele et al. (2015) 10.1016/j.gca.2015.06.032[3] Petersen et al. (2019) 10.1029/2018GC008127[4] Anderson et al. (2021) 10.1029/2020GL092069[5] Da&amp;#235;ron (2021) 10.1029/2020GC009592[6] Bernasconi et al. (2021) 10.1029/2020GC009588
Open and FAIR sample based data sharing through the IEDA2 facility

IEDA, the Interdisciplinary Earth Data Alliance, is a unique collaborative data infrastructure that provides and continuously evolves a comprehensive ecosystem of data, tools, and services that support researchers in the Geosciences to share and access sample data following the FAIR data principles and ensure open, reproducible, and transparent science practices.The &amp;#8216;next generation&amp;#8217; of IEDA - IEDA2 - was funded by the US National Science Foundation in 2022 for 5 years to advance the existing data systems and services of EarthChem (geochemistry data repository, data synthesis, and data access portals), LEPR/TraceDs (synthesis of data from petrological experiments), and SESAR (System for Earth Sample Registration), modernizing system architecture to better support computational and data-driven research, improving usability, and growing a diverse and inclusive user audience through education and engagement.Our target is to enable a common understanding of existing information that is validated through expert data curation to enable transparent and reproducible (re)use and analysis of data, inform peer review, and guide future research directions. Such common understanding cannot develop unless the community can access and assess the same information.This collective vision of the data will improve the ability of the entire community to assess the context and significance of new data and models, allow reviewers to evaluate new models that are calibrated using the data, and facilitate new generations of research.
The impact of training dataset on a vision-based smart road sensor to measure the level of flood on the streets

With recent advancements in Artificial Intelligence (AI) and decreases in computing costs, early warning systems have become affordable and popular in many different applications such as smart cities, and disaster management. AI and the Internet of Things (IoT) enable the automatic detection of different incidences, such as flash flooding. The IoT solutions at the scene of an emergency, enable capturing various data and sharing in real-time with first responders. The accuracy of flood level estimation is critical for proper emergency response. This is while the accuracy depends on multiple factors, such as camera properties, the brightness of the environment, training data size, etc. In this research, we focus on the impact of training dataset size, the number of classes, and the labelling method of training data on the accuracy of the proposed Machine Learning (ML) method. A vision-based smart road sensor was developed to detect flood occurrences in urban areas, which can be used as a sub-module of an early warning system. We used the portion of cars&amp;#8217; tires that is visible to the camera as an indicator to detect and classify the depth of standing water into four classes: &amp;#8220;no-water&amp;#8221;, &amp;#8220;low-level&amp;#8221;, &amp;#8220;high-level&amp;#8221;, and &amp;#8220;water-splash&amp;#8221;. A Convolutional Neural Network (CNN) called You Only Look Once (YOLO) was trained for this matter. The results showed that the accuracy of the trained CNN depends on multiple factors. In this study, we evaluated and studied the impact of the training data size, the number of classes, and the labelling method of training data on the accuracy of the early warning system.