Knowledge-based Sense Disambiguation of Multiword Expressions in Requirements Documents

Understanding the meaning and the senses of expressions is essential to analyze natural language requirements. Disambiguation of expressions in their context is needed to prevent misinterpretations. Current knowledge-based disambiguation approaches only focus on senses of single words and miss out on linking the shared meaning of expressions consisting of multiple words. As these expressions are common in requirements, we propose a sense disambiguation approach that is able to detect and disambiguate multiword expressions. We use a two-tiered approach to be able to use different techniques for detection and disambiguation. Initially, a conditional random field detects multiword expressions. Afterwards, the approach disambiguates these expressions and retrieves the corresponding senses using a knowledge-based approach. The knowledge-based approach has the benefit that only the knowledge base has to be exchanged to adapt the approach to new domains and knowledge. Our approach is able to detect multiword expressions with an $\text{F}_{1}$-score of 88.4% in an evaluation on 997 requirement sentences. The sense disambiguation achieves up to 57% $\text{F}_{1}$-score

Experimental evidence of delocalized states in random dimer superlattices

We study the electronic properties of GaAs-AlGaAs superlattices with intentional correlated disorder by means of photoluminescence and vertical dc resistance. The results are compared to those obtained in ordered and uncorrelated disordered superlattices. We report the first experimental evidence that spatial correlations inhibit localization of states in disordered low-dimensional systems, as our previous theoretical calculations suggested, in contrast to the earlier belief that all eigenstates are localized.Comment: 4 pages, 5 figures. Physical Review Letters (in press

Improving Traceability Link Recovery Using Fine-grained Requirements-to-Code Relations

Traceability information is a fundamental prerequisite for many essential software maintenance and evolution tasks, such as change impact and software reusability analyses. However, manually generating traceability information is costly and error-prone. Therefore, researchers have developed automated approaches that utilize textual similarities between artifacts to establish trace links. These approaches tend to achieve low precision at reasonable recall levels, as they are not able to bridge the semantic gap between high-level natural language requirements and code. We propose to overcome this limitation by leveraging fine-grained, method and sentence level, similarities between the artifacts for traceability link recovery. Our approach uses word embeddings and a Word Mover\u27s Distance-based similarity to bridge the semantic gap. The fine-grained similarities are aggregated according to the artifacts structure and participate in a majority vote to retrieve coarse-grained, requirement-to-class, trace links. In a comprehensive empirical evaluation, we show that our approach is able to outperform state-of-the-art unsupervised traceability link recovery approaches. Additionally, we illustrate the benefits of fine-grained structural analyses to word embedding-based trace link generation

NoRBERT: Transfer Learning for Requirements Classification

Classifying requirements is crucial for automatically handling natural language requirements. The performance of existing automatic classification approaches diminishes when applied to unseen projects because requirements usually vary in wording and style. The main problem is poor generalization. We propose NoRBERT that fine-tunes BERT, a language model that has proven useful for transfer learning. We apply our approach to different tasks in the domain of requirements classification. We achieve similar or better results (F$_1$-scores of up to 94%) on both seen and unseen projects for classifying functional and non-functional requirements on the PROMISE NFR dataset. NoRBERT outperforms recent approaches at classifying nonfunctional requirements subclasses. The most frequent classes are classified with an average F$_1$-score of 87%. In an unseen project setup on a relabeled PROMISE NFR dataset, our approach achieves an improvement of ten percentage points in average F$_1$- score compared to recent approaches. Additionally, we propose to classify functional requirements according to the included concerns, i.e., function, data, and behavior. We labeled the functional requirements in the PROMISE NFR dataset and applied our approach. NoRBERT achieves an F$_1$-score of up to 92%. Overall, NoRBERT improves requirements classification and can be applied to unseen projects with convincing results

Towards Programming in Natural Language: Learning New Functions from Spoken Utterances

Systems with conversational interfaces are rather popular nowadays. However, their full potential is not yet exploited. For the time being, users are restricted to calling predefined functions. Soon, users will expect to customize systems to their needs and create own functions using nothing but spoken instructions. Thus, future systems must understand how laypersons teach new functionality to intelligent systems. The understanding of natural language teaching sequences is a first step toward comprehensive end-user programming in natural language. We propose to analyze the semantics of spoken teaching sequences with a hierarchical classification approach. First, we classify whether an utterance constitutes an effort to teach a new function or not. Afterward, a second classifier locates the distinct semantic parts of teaching efforts: declaration of a new function, specification of intermediate steps, and superfluous information. For both tasks we implement a broad range of machine learning techniques: classical approaches, such as Naïve Bayes, and neural network configurations of various types and architectures, such as bidirectional LSTMs. Additionally, we introduce two heuristic-based adaptations that are tailored to the task of understanding teaching sequences. As data basis we use 3168 descriptions gathered in a user study. For the first task convolutional neural networks obtain the best results (accuracy: 96.6%); bidirectional LSTMs excel in the second (accuracy: 98.8%). The adaptations improve the first-level classification considerably (plus 2.2% points)

What’s the Matter? Knowledge Acquisition by Unsupervised Multi-Topic Labeling for Spoken Utterances

Systems such as Alexa, Cortana, and Siri app ear rather smart. However, they only react to predefined wordings and do not actually grasp the user\u27s intent. To overcome this limitation, a system must understand the topics the user is talking about. Therefore, we apply unsupervised multi-topic labeling to spoken utterances. Although topic labeling is a well-studied task on textual documents, its potential for spoken input is almost unexplored. Our approach for topic labeling is tailored to spoken utterances; it copes with short and ungrammatical input. The approach is two-tiered. First, we disambiguate word senses. We utilize Wikipedia as pre-labeled corpus to train a naïve-bayes classifier. Second, we build topic graphs based on DBpedia relations. We use two strategies to determine central terms in the graphs, i.e. the shared topics. One fo cuses on the dominant senses in the utterance and the other covers as many distinct senses as possible. Our approach creates multiple distinct topics per utterance and ranks results. The evaluation shows that the approach is feasible; the word sense disambiguation achieves a recall of 0.799. Concerning topic labeling, in a user study subjects assessed that in 90.9% of the cases at least one proposed topic label among the first four is a good fit. With regard to precision, the subjects judged that 77.2% of the top ranked labels are a good fit or good but somewhat too broad (Fleiss\u27 kappa κ = 0.27). We illustrate areas of application of topic labeling in the field of programming in spoken language. With topic labeling applied to the spoken input as well as ontologies that model the situational context we are able to select the most appropriate ontologies with an F1-score of 0.907

NoRBERT: Transfer Learning for Requirements Classification

Classifying requirements is crucial for automatically handling natural language requirements. The performance of existing automatic classification approaches diminishes when applied to unseen projects because requirements usually vary in wording and style. The main problem is poor generalization. We propose NoRBERT that fine-tunes BERT, a language model that has proven useful for transfer learning. We apply our approach to different tasks in the domain of requirements classification. We achieve similar or better results $\text{F}_1$-scores of up to 94%) on both seen and unseen projects for classifying functional and non-functional requirements on the PROMISE NFR dataset. NoRBERT outperforms recent approaches at classifying non-functional requirements subclasses. The most frequent classes are classified with an average $\text{F}_1$-score of 87%. In an unseen project setup on a relabeled PROMISE NFR dataset, our approach achieves an improvement of 15 percentage points in average $\text{F}_1$-score compared to recent approaches. Additionally, we propose to classify functional requirements according to the included concerns, i.e., function, data, and behavior. We labeled the functional requirements in the PROMISE NFR dataset and applied our approach. NoRBERT achieves an $\text{F}_1$-score of up to 92%. Overall, NoRBERT improves requirements classification and can be applied to unseen projects with convincing results

Roger that! Learning How Laypersons Teach New Functions to Intelligent Systems

Intelligent systems are rather smart today but still limited to built-in functionality. To break through this barrier, future systems must allow users to easily adapt the system by themselves. For humans the most natural way to communicate is talking. But what if users want to extend the systems’ functionality with nothing but natural language? Then intelligent systems must understand how laypersons teach new skills. To grasp the semantics of such teaching sequences, we have defined a hierarchical classification task. On the first level, we consider the existence of a teaching intent in an utterance; on the second, we classify the distinct semantic parts of teaching sequences: declaration of a new function, specification of intermediate steps, and superfluous information. We evaluate twelve machine learning techniques with multiple configurations tailored to this task ranging from classical approaches such as naı̈ve-bayes to modern techniques such as bidirectional LSTMs and task-oriented adaptations. On the first level convolutional neural networks achieve the best accuracy (96.6%). For the second task, bidirectional LSTMs are the most accurate (98.8%). With the additional adaptations we are able to improve both classifications distinctly (up to 1.8%)

NMDA Induces Long-Term Synaptic Depression and Dephosphorylation of the GluR1 Subunit of AMPA Receptors in Hippocampus

AbstractBrief bath application of N-methyl-D-aspartate (NMDA) to hippocampal slices produces long-term synaptic depression (LTD) in CA1 that is (1) sensitive to postnatal age, (2) saturable, (3) induced postsynaptically, (4) reversible, and (5) not associated with a change in paired pulse facilitation. Chemically induced LTD (Chem-LTD) and homosynaptic LTD are mutually occluding, suggesting a common expression mechanism. Using phosphorylation site–specific antibodies, we found that induction of chem-LTD produces a persistent dephosphorylation of the GluR1 subunit of AMPA receptors at serine 845, a cAMP-dependent protein kinase (PKA) substrate, but not at serine 831, a substrate of protein kinase C (PKC) and calcium/calmodulin-dependent protein kinase II (CaMKII). These results suggest that dephosphorylation of AMPA receptors is an expression mechanism for LTD and indicate an unexpected role of PKA in the postsynaptic modulation of excitatory synaptic transmission

Advancing Science with VGI: Reproducibility and Replicability of Recent Studies using VGI

In scientific research, reproducibility and replicability are requirements to ensure the advancement of our body of knowledge. T his holds true also for VGI - related research and studies. However, the characteristics of VGI suggest particular difficulties in ensuring reproducibility and replicability . In this paper, we aim to examine the current situation in VGI - related research , and identify strategies to ensure realization of its full potential. To do so, we first investigate the different aspects of reprod ucibility and replicability and their impact on VGI - related research . These impacts are different depending on the objectives of the study. Therefore , we examine the study focus of VGI - related research to assess the current body of research and structure o ur assessment . Th is work is based on a rigorous review of the elements of reproducibility and a systematic mapping and analysis of 58 papers on the use of VGI in the crisis management field. Results of our investigation show that reproducibility issues related to data are a serious concern , while reproducibility issues related to analysis methods and processes face fewer challenges. Howe ver, since most studies still focus on analyzing the source data, reproducibility and replicability are still an unsolved problem in VGI - related research. Therefore, we show initiative s tackling the problem, and finally formulate strategies to improve the situatio