Tag Recommendation in Software Information Sites

Abstract—Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers im-prove their performance in software development, maintenance and test processes as software information sites. It is common to see tags in software information sites and many sites allow users to tag various objects with their own words. Users increasingly use tags to describe the most important features of their posted contents or projects. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software in-formation sites. TagCombine has 3 different components: 1. multi-label ranking component which considers tag recommendation as a multi-label learning problem; 2. similarity based rankin

The mediating role of mindfulness, attention and situational awareness on driving performance in a virtual reality underground mine

Load-haul-dumps (LHDs) are used to transport materials in underground mining. Due to the design of LHDs and the design of the mine drifts, these vehicles are implicated in accidents involving other mining equipment, the mining environment and pedestrians. In 2015, the Ontario Ministry of Labour published the Mining Health, Safety and Prevention Review, which recommended that mobile equipment operators need to have a strong situational awareness. Mindfulness training can be used to improve an individual’s situational awareness and attention. Mindfulness is a trait that naturally varies amongst individuals. However, it is a technique that can be taught and with training and practice, a person’s mindfulness levels can improve over time. There has been limited research conducted in the area of mindfulness and workplace health and safety; however, there is evidence to suggest that mindfulness training may be a method to improve workplace safety. This study measured a person’s inherent mindfulness, attention and situational awareness and correlated them against driver’s performance measured from within a computer-based virtual reality underground mine simulator. The simulator, or the Situational Awareness Mining Simulator (SAMS), provided the virtual reality experience of operating an LHD in an underground mine. Perception-response time and collisions frequency were measured within the simulator and used as the measures of driver performance. Situational awareness was measured within the simulator by questioning the participants about physical aspects of the virtual mine, such as signage and colour of various objects. Mindfulness was measured using the Mindfulness Attention Awareness Scale (MAAS) and attention was measured using the Attention-Related Driving Errors Scale (ARDES-US). Participants (n = 21) operated a load-haul-dump in the simulator for two trials, each approximately 15-20 minutes in length. Spearman’s correlations showed a relationship between frequency of collisions and perception-response time (r = .449, p = .05); situational awareness and collision frequency (r = .507, p < .05); and situational awareness and mindfulness (r = .434, p < .05). These correlations were present in either Trial 1 or Trial 2, not both trials and thus, should be interpreted with caution. There was also a significant negative correlation between MAAS and ARDES-US scores (r = -.516, p = <.05). There were no other correlations present between ARDES-US scores and any other variables. This study provides evidence that by cueing individuals to aspects of their surroundings, Level 1 situational awareness (SA) can be increased and further, the relationship between SA and mindfulness becomes more apparent. No evidence was able to suggest a relationship between attention levels, as measured by ARDES-US and driving performance, or situational awareness. The learning curve of adapting to the simulator was substantial, and clouded some of the results, especially pertaining to collision frequency, and situational awareness.Master of Human Kinetics (MHK

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Unsupervised relation extraction for e-learning applications

In this modern era many educational institutes and business organisations are adopting the e-Learning approach as it provides an effective method for educating and testing their students and staff. The continuous development in the area of information technology and increasing use of the internet has resulted in a huge global market and rapid growth for e-Learning. Multiple Choice Tests (MCTs) are a popular form of assessment and are quite frequently used by many e-Learning applications as they are well adapted to assessing factual, conceptual and procedural information. In this thesis, we present an alternative to the lengthy and time-consuming activity of developing MCTs by proposing a Natural Language Processing (NLP) based approach that relies on semantic relations extracted using Information Extraction to automatically generate MCTs. Information Extraction (IE) is an NLP field used to recognise the most important entities present in a text, and the relations between those concepts, regardless of their surface realisations. In IE, text is processed at a semantic level that allows the partial representation of the meaning of a sentence to be produced. IE has two major subtasks: Named Entity Recognition (NER) and Relation Extraction (RE). In this work, we present two unsupervised RE approaches (surface-based and dependency-based). The aim of both approaches is to identify the most important semantic relations in a document without assigning explicit labels to them in order to ensure broad coverage, unrestricted to predefined types of relations. In the surface-based approach, we examined different surface pattern types, each implementing different assumptions about the linguistic expression of semantic relations between named entities while in the dependency-based approach we explored how dependency relations based on dependency trees can be helpful in extracting relations between named entities. Our findings indicate that the presented approaches are capable of achieving high precision rates. Our experiments make use of traditional, manually compiled corpora along with similar corpora automatically collected from the Web. We found that an automatically collected web corpus is still unable to ensure the same level of topic relevance as attained in manually compiled traditional corpora. Comparison between the surface-based and the dependency-based approaches revealed that the dependency-based approach performs better. Our research enabled us to automatically generate questions regarding the important concepts present in a domain by relying on unsupervised relation extraction approaches as extracted semantic relations allow us to identify key information in a sentence. The extracted patterns (semantic relations) are then automatically transformed into questions. In the surface-based approach, questions are automatically generated from sentences matched by the extracted surface-based semantic pattern which relies on a certain set of rules. Conversely, in the dependency-based approach questions are automatically generated by traversing the dependency tree of extracted sentence matched by the dependency-based semantic patterns. The MCQ systems produced from these surface-based and dependency-based semantic patterns were extrinsically evaluated by two domain experts in terms of questions and distractors readability, usefulness of semantic relations, relevance, acceptability of questions and distractors and overall MCQ usability. The evaluation results revealed that the MCQ system based on dependency-based semantic relations performed better than the surface-based one. A major outcome of this work is an integrated system for MCQ generation that has been evaluated by potential end users.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Inferring Narrative Causality between Event Pairs in Films

To understand narrative, humans draw inferences about the underlying relations between narrative events. Cognitive theories of narrative understanding define these inferences as four different types of causality, that include pairs of events A, B where A physically causes B (X drop, X break), to pairs of events where A causes emotional state B (Y saw X, Y felt fear). Previous work on learning narrative relations from text has either focused on "strict" physical causality, or has been vague about what relation is being learned. This paper learns pairs of causal events from a corpus of film scene descriptions which are action rich and tend to be told in chronological order. We show that event pairs induced using our methods are of high quality and are judged to have a stronger causal relation than event pairs from Rel-grams

Holistic recommender systems for software engineering

The knowledge possessed by developers is often not sufficient to overcome a programming problem. Short of talking to teammates, when available, developers often gather additional knowledge from development artifacts (e.g., project documentation), as well as online resources. The web has become an essential component in the modern developer’s daily life, providing a plethora of information from sources like forums, tutorials, Q&A websites, API documentation, and even video tutorials. Recommender Systems for Software Engineering (RSSE) provide developers with assistance to navigate the information space, automatically suggest useful items, and reduce the time required to locate the needed information. Current RSSEs consider development artifacts as containers of homogeneous information in form of pure text. However, text is a means to represent heterogeneous information provided by, for example, natural language, source code, interchange formats (e.g., XML, JSON), and stack traces. Interpreting the information from a pure textual point of view misses the intrinsic heterogeneity of the artifacts, thus leading to a reductionist approach. We propose the concept of Holistic Recommender Systems for Software Engineering (H-RSSE), i.e., RSSEs that go beyond the textual interpretation of the information contained in development artifacts. Our thesis is that modeling and aggregating information in a holistic fashion enables novel and advanced analyses of development artifacts. To validate our thesis we developed a framework to extract, model and analyze information contained in development artifacts in a reusable meta- information model. We show how RSSEs benefit from a meta-information model, since it enables customized and novel analyses built on top of our framework. The information can be thus reinterpreted from an holistic point of view, preserving its multi-dimensionality, and opening the path towards the concept of holistic recommender systems for software engineering

Synonym Detection Using Syntactic Dependency And Neural Embeddings

Recent advances on the Vector Space Model have significantly improved some NLP applications such as neural machine translation and natural language generation. Although word co-occurrences in context have been widely used in counting-/predicting-based distributional models, the role of syntactic dependencies in deriving distributional semantics has not yet been thoroughly investigated. By comparing various Vector Space Models in detecting synonyms in TOEFL, we systematically study the salience of syntactic dependencies in accounting for distributional similarity. We separate syntactic dependencies into different groups according to their various grammatical roles and then use context-counting to construct their corresponding raw and SVD-compressed matrices. Moreover, using the same training hyperparameters and corpora, we study typical neural embeddings in the evaluation. We further study the effectiveness of injecting human-compiled semantic knowledge into neural embeddings on computing distributional similarity. Our results show that the syntactically conditioned contexts can interpret lexical semantics better than the unconditioned ones, whereas retrofitting neural embeddings with semantic knowledge can significantly improve synonym detection