Graph-Based Keyphrase Extraction for Software Traceability in Source Code and Documentation Mapping

Natural Language Processing (NLP) forms the basis of several computational tasks. However,  when applied to the software system’s, NLP provides several irrelevant features and the noise gets mixed up while extracting features. As the scale of software system’s increases,   different   metrics are needed to assess these systems. Diagrammatic and visual representation of the SE projects code forms an essential component of Source Code Analysis (SCA). These SE projects cannot be analyzed by traditional source code analysis methods nor can they be analyzed by traditional diagrammatic representation. Hence, there is a need to modify the traditional approaches in lieu of changing environments to reduce learning gap for the developers and traceability engineers. The traditional approaches fall short in addressing specific metrics in terms of document similarity and graph dependency approaches. In terms of source code analysis, the graph dependency graph can be used for finding the relevant key-terms and keyphrases as they occur not just intra-document but also inter-document. In this work, a similarity measure based on context is proposed which can be employed to find a traceability link between the source code metrics and API documents present in a package.   Probabilistic graph-based keyphrase extraction approach is used for searching across the different project files.&nbsp

Visual Summarization of Scholarly Videos using Word Embeddings and Keyphrase Extraction

Effective learning with audiovisual content depends on many factors. Besides the quality of the learning resource's content, it is essential to discover the most relevant and suitable video in order to support the learning process most effectively. Video summarization techniques facilitate this goal by providing a quick overview over the content. It is especially useful for longer recordings such as conference presentations or lectures. In this paper, we present an approach that generates a visual summary of video content based on semantic word embeddings and keyphrase extraction. For this purpose, we exploit video annotations that are automatically generated by speech recognition and video OCR (optical character recognition).Comment: 12 pages, 5 figure

Contextual question answering for the health domain

Studies have shown that natural language interfaces such as question answering and conversational systems allow information to be accessed and understood more easily by users who are unfamiliar with the nuances of the delivery mechanisms (e.g., keyword-based search engines) or have limited literacy in certain domains (e.g., unable to comprehend health-related content due to terminology barrier). In particular, the increasing use of the web for health information prompts us to reexamine our existing delivery mechanisms. We present enquireMe, which is a contextual question answering system that provides lay users with the ability to obtain responses about a wide range of health topics by vaguely expressing at the start and gradually refining their information needs over the course of an interaction session using natural language. enquireMe allows the users to engage in 'conversations' about their health concerns, a process that can be therapeutic in itself. The system uses community-driven question-answer pairs from the web together with a decay model to deliver the top scoring answers as responses to the users' unrestricted inputs. We evaluated enquireMe using benchmark data from WebMD and TREC to assess the accuracy of system-generated answers. Despite the absence of complex knowledge acquisition and deep language processing, enquireMe is comparable to the state-of-the-art question answering systems such as START as well as those interactive systems from TREC

Advances in Automatic Keyphrase Extraction

The main purpose of this thesis is to analyze and propose new improvements in the field of Automatic Keyphrase Extraction, i.e., the field of automatically detecting the key concepts in a document. We will discuss, in particular, supervised machine learning algorithms for keyphrase extraction, by first identifying their shortcomings and then proposing new techniques which exploit contextual information to overcome them. Keyphrase extraction requires that the key concepts, or \emph{keyphrases}, appear verbatim in the body of the document. We will identify the fact that current algorithms do not use contextual information when detecting keyphrases as one of the main shortcomings of supervised keyphrase extraction. Instead, statistical and positional cues, like the frequency of the candidate keyphrase or its first appearance in the document, are mainly used to determine if a phrase appearing in a document is a keyphrase or not. For this reason, we will prove that a supervised keyphrase extraction algorithm, by using only statistical and positional features, is actually able to extract good keyphrases from documents written in languages that it has never seen. The algorithm will be trained over a common dataset for the English language, a purpose-collected dataset for the Arabic language, and evaluated on the Italian, Romanian and Portuguese languages as well. This result is then used as a starting point to develop new algorithms that use contextual information to increase the performance in automatic keyphrase extraction. The first algorithm that we present uses new linguistics features based on anaphora resolution, which is a field of natural language processing that exploits the relations between elements of the discourse as, e.g., pronouns. We evaluate several supervised AKE pipelines based on these features on the well-known SEMEVAL 2010 dataset, and we show that the performance increases when we add such features to a model that employs statistical and positional knowledge only. Finally, we investigate the possibilities offered by the field of Deep Learning, by proposing six different deep neural networks that perform automatic keyphrase extraction. Such networks are based on bidirectional long-short term memory networks, or on convolutional neural networks, or on a combination of both of them, and on a neural language model which creates a vector representation of each word of the document. These networks are able to learn new features using the the whole document when extracting keyphrases, and they have the advantage of not needing a corpus after being trained to extract keyphrases from new documents. We show that with deep learning based architectures we are able to outperform several other keyphrase extraction algorithms, both supervised and not supervised, used in literature and that the best performances are obtained when we build an additional neural representation of the input document and we append it to the neural language model. Both the anaphora-based and the deep-learning based approaches show that using contextual information, the performance in supervised algorithms for automatic keyphrase extraction improves. In fact, in the methods presented in this thesis, the algorithms which obtained the best performance are the ones receiving more contextual information, both about the relations of the potential keyphrase with other parts of the document, as in the anaphora based approach, and in the shape of a neural representation of the input document, as in the deep learning approach. In contrast, the approach of using statistical and positional knowledge only allows the building of language agnostic keyphrase extraction algorithms, at the cost of decreased precision and recall

Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

The extraction of high-quality keywords and sum-marising documents at a high level has become more difficult in current research due to technological advancements and the expo-nential expansion of textual data and digital sources. Extracting high-quality keywords and summarising the documents at a high-level need to use features for the keyphrase extraction, becoming more popular. A new unsupervised keyphrase concentrated area (KCA) identification approach is proposed in this study as a feature of keyphrase extraction: corpus, domain and language independent; document length-free; utilized by both supervised and unsupervised techniques. In the proposed system, there are three phases: data pre-processing, data processing, and KCA identification. The system employs various text pre-processing methods before transferring the acquired datasets to the data processing step. The pre-processed data is subsequently used during the data processing step. The statistical approaches, curve plotting, and curve fitting technique are applied in the KCA identification step. The proposed system is then tested and evaluated using benchmark datasets collected from various sources. To demonstrate our proposed approach’s effectiveness, merits, and significance, we compared it with other proposed techniques. The experimental results on eleven (11) datasets show that the proposed approach effectively recognizes the KCA from articles as well as significantly enhances the current keyphrase extraction methods based on various text sizes, languages, and domains

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010

A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

Automated keyphrase extraction is crucial for extracting and summarizing relevant information from a variety of publications in multiple domains. However, the extraction of good-quality keyphrases and the summarising of information to a good standard have become extremely challenging in recent research because of the advancement of technology and the exponential development of digital sources and textual information. Because of this, the usage of keyphrase features for keyphrase extraction techniques has recently gained tremendous popularity. This paper proposed a new unsupervised region-based keyphrase centroid and frequency analysis technique, named the KCFA technique, for keyphrase extraction as a feature. Data/datasets collection, data pre-processing, statistical methodologies, curve plotting analysis, and curve fitting technique are the five main processes in the proposed technique. To begin, the technique collects multiple datasets from diverse sources, which are then input into the data pre-processing step by utilizing some text pre-processing processes. Afterward, the region-based statistical methodologies receive the pre-processed data, followed by the curve plotting examination and, lastly, the curve fitting technique. The proposed technique is then tested and evaluated using ten (10) best-accessible benchmark datasets from various disciplines. The proposed approach is then compared to our available methods to demonstrate its efficacy, advantages, and importance. Lastly, the results of the experiment show that the proposed method works well to analyze the centroid and frequency of keyphrases from academic articles. It provides a centroid of 706.66 and a frequency of 38.95% in the first region, 2454.21 and 7.98% in the second region, for a total frequency of 68.11

Knowledge-Based Techniques for Scholarly Data Access: Towards Automatic Curation

Accessing up-to-date and quality scientific literature is a critical preliminary step in any research activity. Identifying relevant scholarly literature for the extents of a given task or application is, however a complex and time consuming activity. Despite the large number of tools developed over the years to support scholars in their literature surveying activity, such as Google Scholar, Microsoft Academic search, and others, the best way to access quality papers remains asking a domain expert who is actively involved in the field and knows research trends and directions. State of the art systems, in fact, either do not allow exploratory search activity, such as identifying the active research directions within a given topic, or do not offer proactive features, such as content recommendation, which are both critical to researchers. To overcome these limitations, we strongly advocate a paradigm shift in the development of scholarly data access tools: moving from traditional information retrieval and filtering tools towards automated agents able to make sense of the textual content of published papers and therefore monitor the state of the art. Building such a system is however a complex task that implies tackling non trivial problems in the fields of Natural Language Processing, Big Data Analysis, User Modelling, and Information Filtering. In this work, we introduce the concept of Automatic Curator System and present its fundamental components.openDottorato di ricerca in InformaticaopenDe Nart, Dari

An investigation into the use of negation in Inductive Rule Learning for text classification

This thesis seeks to establish if the use of negation in Inductive Rule Learning (IRL) for text classification is effective. Text classification is a widely research topic in the domain of data mining. There have been many techniques directed at text classification; one of them is IRL, widely chosen because of its simplicity, comprehensibility and interpretability by humans. IRL is a process whereby rules in the form of $antecedent -> conclusion$ are learnt to build a classifier. Thus, the learnt classifier comprises a set of rules, which are used to perform classification. To learn a rule, words from pre-labelled documents, known as features, are selected to be used as conjunctions in the rule antecedent. These rules typically do not include any negated features in their antecedent; although in some cases, as demonstrated in this thesis, the inclusion of negation is required and beneficial for the text classification task. With respect to the use of negation in IRL, two issues need to be addressed: (i) the identification of the features to be negated and (ii) the improvisation of rule refinement strategies to generate rules both with and without negation. To address the first issue, feature space division is proposed, whereby the feature space containing features to be used for rule refinement is divided into three sub-spaces to facilitate the identification of the features which can be advantageously negated. To address the second issue, eight rule refinement strategies are proposed, which are able to generate both rules with and without negation. Typically, single keywords which are deemed significant to differentiate between classes are selected to be used in the text representation in the text classification task. Phrases have also been proposed because they are considered to be semantically richer than single keywords. Therefore, with respect to the work conducted in this thesis, three different types of phrases ($n$-gram phrases, keyphrases and fuzzy phrases) are extracted to be used as the text representation in addition to the use of single keywords. To establish the effectiveness of the use of negation in IRL, the eight proposed rule refinement strategies are compared with one another, using keywords and the three different types of phrases as the text representation, to determine whether the best strategy is one which generates rules with negation or without negation. Two types of classification tasks are conducted; binary classification and multi-class classification. The best strategy in the proposed IRL mechanism is compared to five existing text classification techniques with respect to binary classification: (i) the Sequential Minimal Optimization (SMO) algorithm, (ii) Naive Bayes (NB), (iii) JRip, (iv) OlexGreedy and (v) OlexGA from the Waikato Environment for Knowledge Analysis (WEKA) machine learning workbench. In the multi-class classification task, the proposed IRL mechanism is compared to the Total From Partial Classification (TFPC) algorithm. The datasets used in the experiments include three text datasets: 20 Newsgroups, Reuters-21578 and Small Animal Veterinary Surveillance Network (SAVSNET) datasets and five UCI Machine Learning Repository tabular datasets. The results obtained from the experiments showed that the strategies which generated rules with negation were more effective when the keyword representation was used and less prominent when the phrase representations were used. Strategies which generated rules with negation also performed better with respect to binary classification compared to multi-class classification. In comparison with the other machine learning techniques selected, the proposed IRL mechanism was shown to generally outperform all the compared techniques and was competitive with SMO