Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Analytical Chemistry
Title: Highly sensitive MoTe
                    2
                    chemical sensor with fast recovery rate through gate biasing
Abstract: The unique properties of two dimensional (2D) materials make them promising candidates for chemical and biological sensing applications. However, most 2D nanomaterial sensors suffer very long recovery time due to slow molecular desorption at room temperature. Here, we report a highly sensitive molybdenum ditelluride (MoTe2) gas sensor for NO2 and NH3 detection with greatly enhanced recovery rate. The effects of gate bias on sensing performance have been systematically studied. It is found that the recovery kinetics can be effectively adjusted by biasing the sensor to different gate voltages. Under the optimum biasing potential, the MoTe2 sensor can achieve more than 90% recovery after each sensing cycle well within 10 min at room temperature. The results demonstrate the potential of MoTe2 as a promising candidate for high-performance chemical sensors. The idea of exploiting gate bias to adjust molecular desorption kinetics can be readily applied to much wider sensing platforms based on 2D nanomaterials.
Extracted statement — has research problem: Chemical sensors
Research field: Analytical Chemistry
Title: Flower-like Palladium Nanoclusters Decorated Graphene Electrodes for Ultrasensitive and Flexible Hydrogen Gas Sensing
Abstract: Abstract Flower-like palladium nanoclusters (FPNCs) are electrodeposited onto graphene electrode that are prepared by chemical vapor deposition (CVD). The CVD graphene layer is transferred onto a poly(ethylene naphthalate) (PEN) film to provide a mechanical stability and flexibility. The surface of the CVD graphene is functionalized with diaminonaphthalene (DAN) to form flower shapes. Palladium nanoparticles act as templates to mediate the formation of FPNCs, which increase in size with reaction time. The population of FPNCs can be controlled by adjusting the DAN concentration as functionalization solution. These FPNCs_CG electrodes are sensitive to hydrogen gas at room temperature. The sensitivity and response time as a function of the FPNCs population are investigated, resulted in improved performance with increasing population. Furthermore, the minimum detectable level (MDL) of hydrogen is 0.1 ppm, which is at least 2 orders of magnitude lower than that of chemical sensors based on other Pd-based hybrid materials.
Extracted statement — has research problem: Chemical sensors
Research field: Artificial Intelligence
Title: Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas
Abstract: This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas. The shared task featured two independent tracks, and participants submitted machine translation systems for up to 10 indigenous languages. Overall, 8 teams participated with a total of 214 submissions. We provided training sets consisting of data collected from various sources, as well as manually translated sentences for the development and test sets. An official baseline trained on this data was also provided. Team submissions featured a variety of architectures, including both statistical and neural models, and for the majority of languages, many teams were able to considerably improve over the baseline. The best performing systems achieved 12.97 ChrF higher than baseline, when averaged across languages.
Extracted statement — has research problem:  Open Machine Translation
Research field: Artificial Intelligence
Title: Discourse Trees Are Good Indicators of Importance in Text
Abstract: Researchers in computational linguistics have long speculated that the nuclei of the rhetorical structure tree of a text form an adequate \summary" of the text for which that tree was built. However, to my knowledge, there has been no experiment to connrm how valid this speculation really is. In this paper, I describe a psycholinguistic experiment that shows that the concepts of discourse structure and nuclearity can be used eeectively in text summarization. More precisely, I show that there is a strong correlation between the nuclei of the discourse structure of a text and what readers perceive to be the most important units in that text. In addition, I propose and evaluate the quality of an automatic, discourse-based summa-rization system that implements the methods that were validated by the psycholinguistic experiment. The evaluation indicates that although the system does not match yet the results that would be obtained if discourse trees had been built manually, it still signiicantly outperforms both a baseline algorithm and Microsoft's OOce97 summarizer. 1 Motivation Traditionally, previous approaches to automatic text summarization have assumed that the salient parts of a text can be determined by applying one or more of the following assumptions: important sentences in a text contain words that are used frequently (Luhn 1958; Edmundson 1968); important sentences contain words that are used in the title and section headings (Edmundson 1968); important sentences are located at the beginning or end of paragraphs (Baxendale 1958); important sentences are located at positions in a text that are genre dependent, and these positions can be determined automatically, through training important sentences use bonus words such as \greatest" and \signiicant" or indicator phrases such as \the main aim of this paper" and \the purpose of this article", while unimportant sentences use stigma words such as \hardly" and \im-possible" important sentences and concepts are the highest connected entities in elaborate semantic struc-important and unimportant sentences are derivable from a discourse representation of the text (Sparck Jones 1993b; Ono, Sumita, & Miike 1994). In determining the words that occur most frequently in a text or the sentences that use words that occur in the headings of sections, computers are accurate tools. Therefore, in testing the validity of using these indicators for determining the most important units in a text, it is adequate to compare the direct output of a summarization program that implements the assump-tion(s) under scrutiny with a human-made …
Extracted statement — has research problem: Automatic text summarization
Research field: Artificial Intelligence
Title: A Statistical Approach for Automatic Text Summarization by Extraction
Abstract: Automatic Document Summarization is a highly interdisciplinary research area related with computer science as well as cognitive psychology. This Summarization is to compress an original document into a summarized version by extracting almost all of the essential concepts with text mining techniques. This research focuses on developing a statistical automatic text summarization approach, Kmixture probabilistic model, to enhancing the quality of summaries. KSRS employs the K-mixture probabilistic model to establish term weights in a statistical sense, and further identifies the term relationships to derive the semantic relationship significance (SRS) of nouns. Sentences are ranked and extracted based on their semantic relationship significance values. The objective of this research is thus to propose a statistical approach to text summarization. We propose a K-mixture semantic relationship significance (KSRS) approach to enhancing the quality of document summary results. The K-mixture probabilistic model is used to determine the term weights. Term relationships are then investigated to develop the semantic relationship of nouns that manifests sentence semantics. Sentences with significant semantic relationship, nouns are extracted to form the summary accordingly.
Extracted statement — has research problem: Automatic text summarization
Research field: Artificial Intelligence
Title: Overview of NLPTEA-2020 Shared Task for Chinese Grammatical Error Diagnosis
Abstract: This paper presents the NLPTEA 2020 shared task for Chinese Grammatical Error Diagnosis (CGED) which seeks to identify grammatical error types, their range of occurrence and recommended corrections within sentences written by learners of Chinese as a foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 30 teams registered for this shared task, 17 teams developed the system and submitted a total of 43 runs. System performances achieved a significant progress, reaching F1 of 91% in detection level, 40% in position level and 28% in correction level. All data sets with gold standards and scoring scripts are made publicly available to researchers.
Extracted statement — has research problem: Chinese Grammatical Error Diagnosis
Research field: Artificial Intelligence
Title: SemEval-2012 Task 5: Chinese Semantic Dependency Parsing
Abstract: The paper presents the SemEval-2012 Shared Task 5: Chinese Semantic Dependency Parsing. The goal of this task is to identify the dependency structure of Chinese sentences from the semantic view. We firstly introduce the motivation of providing Chinese semantic dependency parsing task, and then describe the task in detail including data preparation, data format, task evaluation, and so on. Over ten thousand sentences were labeled for participants to train and evaluate their systems. At last, we briefly describe the submitted systems and analyze these results.
Extracted statement — has research problem: Chinese Semantic Dependency Parsing
Research field: Artificial Intelligence
Title: Conversational Neuro-Symbolic Commonsense Reasoning
Abstract: One aspect of human commonsense reasoning is the ability to make presumptions about daily experiences, activities and social interactions with others. We propose a new commonsense reasoning benchmark where the task is to uncover commonsense presumptions implied by imprecisely stated natural language commands in the form of if-then-because statements. For example, in the command "If it snows at night then wake me up early because I don't want to be late for work" the speaker relies on commonsense reasoning of the listener to infer the implicit presumption that it must snow enough to cause traffic slowdowns. Such if-then-because commands are particularly important when users instruct conversational agents. We release a benchmark data set for this task, collected from humans and annotated with commonsense presumptions. We develop a neuro-symbolic theorem prover that extracts multi-hop reasoning chains and apply it to this problem. We further develop an interactive conversational framework that evokes commonsense knowledge from humans for completing reasoning chains.
Extracted statement — has research problem: commonsense reasoning
Research field: Artificial Intelligence
Title: SemEval-2020 Task 4: Commonsense Validation and Explanation
Abstract: In this paper, we present SemEval-2020 Task 4,CommonsenseValidation andExplanation(ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish anatural language statement thatmakes senseto humans from one that does not, and provide thereasons. Specifically, in our first subtask, the participating systems are required to choose from twonatural language statements of similar wording the one thatmakes senseand the one does not. Thesecond subtask additionally asks a system to select the key reason from three options why a givenstatement does not make sense. In the third subtask, a participating system needs to generate thereason automatically. 39 teams submitted their valid systems to at least one subtask. For SubtaskA and Subtask B, top-performing teams have achieved results closed to human performance.However, for Subtask C, there is still a considerable gap between system and human performance.The dataset used in our task can be found athttps://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation.
Extracted statement — has research problem: ComVE
Research field: Artificial Intelligence
Title: SemEval-2010 Task 1: Coreference Resolution in Multiple Languages
Abstract: This paper presents the task "Coreference Resolution in Multiple Languages" to be run in SemEval-2010 (5th International Workshop on Semantic Evaluations). This task aims to evaluate and compare automatic coreference resolution systems for three different languages (Catalan, English, and Spanish) by means of two alternative evaluation metrics, thus providing an insight into (i) the portability of coreference resolution systems across languages, and (ii) the effect of different scoring metrics on ranking the output of the participant systems.
Extracted statement — has research problem: Coreference Resolution
Research field: Artificial Intelligence
Title: SemEval-2020 Task 5: Counterfactual Recognition
Abstract: We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent). Counterfactual thinking is an important characteristic of the human cognitive system; it connects antecedents and consequent with causal relations. Our task provides a benchmark for counterfactual recognition in natural language with two subtasks. Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not. Subtask-2 requires the participating systems to extract the antecedent and consequent in a given counterfactual statement. During the SemEval-2020 official evaluation period, we received 27 submissions to Subtask-1 and 11 to Subtask-2. Our data and baseline code are made publicly available at https://zenodo.org/record/3932442. The task website and leaderboard can be found at https://competitions.codalab.org/competitions/21691.
Extracted statement — has research problem: Counterfactual recognition
Research field: Artificial Intelligence
Title: SemEval-2010 Task 2: Cross-Lingual Lexical Substitution
Abstract: In this paper we describe the SemEval-2010 Cross-Lingual Lexical Substitution task, where given an English target word in context, participating systems had to find an alternative substitute word or phrase in Spanish. The task is based on the English Lexical Substitution task run at SemEval-2007. In this paper we provide background and motivation for the task, we describe the data annotation process and the scoring system, and present the results of the participating systems.
Extracted statement — has research problem: Cross-Lingual Lexical Substitution
Research field: Artificial Intelligence
Title: Semeval-2013 Task 8: Cross-lingual Textual Entailment for Content Synchronization
Abstract: This paper presents the second round of the task on Cross-lingual Textual Entailment for Content Synchronization, organized within SemEval-2013. The task was designed to promote research on semantic inference over texts written in different languages, targeting at the same time a real application scenario. Participants were presented with datasets for different language pairs, where multi-directional entailment relations (“forward”, “backward”, “bidirectional”, “no entailment”) had to be identified. We report on the training and test data used for evaluation, the process of their creation, the participating systems (six teams, 61 runs), the approaches adopted and the results achieved.
Extracted statement — has research problem: Cross-lingual Textual Entailment
Research field: Artificial Intelligence
Title: SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases
Abstract: We present a shared task on automatically determining sentiment intensity of a word or a phrase. The words and phrases are taken from three domains: general English, English Twitter, and Arabic Twitter. The phrases include those composed of negators, modals, and degree adverbs as well as phrases formed by words with opposing polarities. For each of the three domains, we assembled the datasets that include multi-word phrases and their constituent words, both manually annotated for real-valued sentiment intensity scores. The three datasets were presented as the test sets for three separate tasks (each focusing on a specific domain). Five teams submitted nine system outputs for the three tasks. All datasets created for this shared task are freely available to the research community.
Extracted statement — has research problem: Determining Sentiment Intensity
Research field: Artificial Intelligence
Title: SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media
Abstract: In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media. The goal of this shared task is to design automatic methods for emphasis selection, i.e. choosing candidates for emphasis in textual content to enable automated design assistance in authoring. The main focus is on short text instances for social media, with a variety of examples, from social media posts to inspirational quotes. Participants were asked to model emphasis using plain text with no additional context from the user or other design considerations. SemEval-2020 Emphasis Selection shared task attracted 197 participants in the early phase and a total of 31 teams made submissions to this task. The highest-ranked submission achieved 0.823 Matchm score. The analysis of systems submitted to the task indicates that BERT and RoBERTa were the most common choice of pre-trained models used, and part of speech tag (POS) was the most useful feature. Full results can be found on the task’s website.
Extracted statement — has research problem: Emphasis Selection for Written Text
Research field: Artificial Intelligence
Title: End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Abstract: We present a novel end-to-end neural model to extract entities and relations between them. Our recurrent neural network based model captures both word sequence and dependency tree substructure information by stacking bidirectional tree-structured LSTM-RNNs on bidirectional sequential LSTM-RNNs. This allows our model to jointly represent both entities and relations with shared parameters in a single model. We further encourage detection of entities during training and use of entity information in relation extraction via entity pretraining and scheduled sampling. Our model improves over the state-of-the-art feature-based model on end-to-end relation extraction, achieving 12.1% and 5.7% relative error reductions in F1-score on ACE2005 and ACE2004, respectively. We also show that our LSTM-RNN based model compares favorably to the state-of-the-art CNN based model (in F1-score) on nominal relation classification (SemEval-2010 Task 8). Finally, we present an extensive ablation analysis of several model components.
Extracted statement — has research problem: End-to-end Relation Extraction
Research field: Artificial Intelligence
Title: SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums
Abstract: We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks. Subtask A is about deciding whether a question asks for factual information vs. an opinion/advice vs. just socializing. Subtask B asks to predict whether an answer to a factual question is true, false or not a proper answer. We received 17 official submissions for subtask A and 11 official submissions for Subtask B. For subtask A, all systems improved over the majority class baseline. For Subtask B, all systems were below a majority class baseline, but several systems were very close to it. The leaderboard and the data from the competition can be found at http://competitions.codalab.org/competitions/20022.
Extracted statement — has research problem: Fact Checking in Community Question Answering Forums
Research field: Artificial Intelligence
Title: Fine-grained Event Classification in News-like Text Snippets - Shared Task 2, CASE 2021
Abstract: This paper describes the Shared Task on Fine-grained Event Classification in News-like Text Snippets. The Shared Task is divided into three sub-tasks: (a) classification of text snippets reporting socio-political events (25 classes) for which vast amount of training data exists, although exhibiting different structure and style vis-a-vis test data, (b) enhancement to a generalized zero-shot learning problem, where 3 additional event types were introduced in advance, but without any training data (‘unseen’ classes), and (c) further extension, which introduced 2 additional event types, announced shortly prior to the evaluation phase. The reported Shared Task focuses on classification of events in English texts and is organized as part of the Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), co-located with the ACL-IJCNLP 2021 Conference. Four teams participated in the task. Best performing systems for the three aforementioned sub-tasks achieved 83.9%, 79.7% and 77.1% weighted F1 scores respectively.
Extracted statement — has research problem: Fine-grained Event Classification
Research field: Artificial Intelligence
Title: Image-Based Food Calorie Estimation Using Knowledge on Food Categories, Ingredients and Cooking Directions
Abstract: Image-based food calorie estimation is crucial to diverse mobile applications for recording everyday meal. However, some of them need human help for calorie estimation, and even if it is automatic, food categories are often limited or images from multiple viewpoints are required. Then, it is not yet achieved to estimate food calorie with practical accuracy and estimating food calories from a food photo is an unsolved problem. Therefore, in this paper, we propose estimating food calorie from a food photo by simultaneous learning of food calories, categories, ingredients and cooking directions using deep learning. Since there exists a strong correlation between food calories and food categories, ingredients and cooking directions information in general, we expect that simultaneous training of them brings performance boosting compared to independent single training. To this end, we use a multi-task CNN [1]. In addition, in this research, we construct two kinds of datasets that is a dataset of calorie-annotated recipe collected from Japanese recipe sites on the Web and a dataset collected from an American recipe site. In this experiment, we trained multi-task and single-task CNNs. As a result, the multi-task CNN achieved the better performance on both food category estimation and food calorie estimation than single-task CNNs. For the Japanese recipe dataset, by introducing a multi-task CNN, 0.039 were improved on the correlation coefficient, while for the American recipe dataset, 0.090 were raised compared to the result by the single-task CNN.
Extracted statement — has research problem: Food calorie estimation