On the Effect of Semantically Enriched Context Models on Software Modularization

Many of the existing approaches for program comprehension rely on the linguistic information found in source code, such as identifier names and comments. Semantic clustering is one such technique for modularization of the system that relies on the informal semantics of the program, encoded in the vocabulary used in the source code. Treating the source code as a collection of tokens loses the semantic information embedded within the identifiers. We try to overcome this problem by introducing context models for source code identifiers to obtain a semantic kernel, which can be used for both deriving the topics that run through the system as well as their clustering. In the first model, we abstract an identifier to its type representation and build on this notion of context to construct contextual vector representation of the source code. The second notion of context is defined based on the flow of data between identifiers to represent a module as a dependency graph where the nodes correspond to identifiers and the edges represent the data dependencies between pairs of identifiers. We have applied our approach to 10 medium-sized open source Java projects, and show that by introducing contexts for identifiers, the quality of the modularization of the software systems is improved. Both of the context models give results that are superior to the plain vector representation of documents. In some cases, the authoritativeness of decompositions is improved by 67%. Furthermore, a more detailed evaluation of our approach on JEdit, an open source editor, demonstrates that inferred topics through performing topic analysis on the contextual representations are more meaningful compared to the plain representation of the documents. The proposed approach in introducing a context model for source code identifiers paves the way for building tools that support developers in program comprehension tasks such as application and domain concept location, software modularization and topic analysis

A question-answering machine learning system for FAQs

With the increase in usage and dependence on the internet for gathering information, it’s now essential to efficiently retrieve information according to users’ needs. Question Answering (QA) systems aim to fulfill this need by trying to provide the most relevant answer for a user’s query expressed in natural language text or speech. Virtual assistants like Apple Siri and automated FAQ systems have become very popular and with this the constant rush of developing an efficient, advanced and expedient QA system is reaching new limits. In the field of QA systems, this thesis addresses the problem of finding the FAQ question that is most similar to a user’s query. Finding semantic similarities between database question banks and natural language text is its foremost step. The work aims at exploring unsupervised approaches for measuring semantic similarities for developing a closed domain QA system. To meet this objective modern sentence representation techniques, such as BERT and FLAIR GloVe, are coupled with various similarity measures (cosine, Euclidean and Manhattan) to identify the best model. The developed models were tested with three FAQs and SemEval 2015 datasets for English language; the best results were obtained from the coupling of BERT embedding with Euclidean distance similarity measure with a performance of 85.956% on a FAQ dataset. The model is also tested for Portuguese language with Portuguese Health support phone line SNS24 dataset; Sumário: Um sistema de pergunta-resposta de aprendizagem automatica para FAQs Com o aumento da utilização e da dependência da internet para a recolha de informação, tornou-se essencial recuperar a informação de forma eficiente de acordo com as necessidades dos utilizadores. Os Sistemas de Pergunta- Resposta (PR) visam responder a essa necessidade, tentando fornecer a resposta mais relevante para a consulta de um utilizador expressa em texto em linguagem natural escrita ou falada. Os assistentes virtuais como o Apple Siri e sistemas automatizados de perguntas frequentes tornaram-se muito populares aumentando a necessidade de desenvolver um sistema de controle de qualidade eficiente, avançado e conveniente. No campo dos sistemas de PR, esta dissertação aborda o problema de encontrar a pergunta que mais se assemelha à consulta de um utilizador. Encontrar semelhanças semânticas entre a base de dados de perguntas e o texto em linguagem natural é a sua etapa mais importante. Neste sentido, esta dissertação tem como objetivo explorar abordagens não supervisionadas para medir similaridades semânticas para o desenvolvimento de um sistema de pergunta-resposta de domínio fechado. Neste sentido, técnicas modernas de representação de frases como o BERT e FLAIR GloVe são utilizadas em conjunto com várias medidas de similaridade (cosseno, Euclidiana e Manhattan) para identificar os melhores modelos. Os modelos desenvolvidos foram testados com conjuntos de dados de três FAQ e o SemEval 2015; os melhores resultados foram obtidos da combinação entre modelos de embedding BERT e a distância euclidiana, tendo-se obtido um desempenho máximo de 85,956% num conjunto de dados FAQ. O modelo também é testado para a língua portuguesa com o conjunto de dados SNS24 da linha telefónica de suporte de saúde em português

Diagnosing Reading strategies: Paraphrase Recognition

Paraphrase recognition is a form of natural language processing used in tutoring, question answering, and information retrieval systems. The context of the present work is an automated reading strategy trainer called iSTART (Interactive Strategy Trainer for Active Reading and Thinking). The ability to recognize the use of paraphrase—a complete, partial, or inaccurate paraphrase; with or without extra information—in the student\u27s input is essential if the trainer is to give appropriate feedback. I analyzed the most common patterns of paraphrase and developed a means of representing the semantic structure of sentences. Paraphrases are recognized by transforming sentences into this representation and comparing them. To construct a precise semantic representation, it is important to understand the meaning of prepositions. Adding preposition disambiguation to the original system improved its accuracy by 20%. The preposition sense disambiguation module itself achieves about 80% accuracy for the top 10 most frequently used prepositions. The main contributions of this work to the research community are the preposition classification and generalized preposition disambiguation processes, which are integrated into the paraphrase recognition system and are shown to be quite effective. The recognition model also forms a significant part of this contribution. The present effort includes the modeling of the paraphrase recognition process, featuring the Syntactic-Semantic Graph as a sentence representation, the implementation of a significant portion of this design demonstrating its effectiveness, the modeling of an effective preposition classification based on prepositional usage, the design of the generalized preposition disambiguation module, and the integration of the preposition disambiguation module into the paraphrase recognition system so as to gain significant improvement

Automatic Identification of False Friends in Parallel Corpora: Statistical and Semantic Approach

False friends are pairs of words in two languages that are perceived as similar but have different meanings. We present an improved algorithm for acquiring false friends from sentence-level aligned parallel corpus based on statistical observations of words occurrences and co-occurrences in the parallel sentences. The results are compared with an entirely semantic measure for cross-lingual similarity between words based on using the Web as a corpus through analyzing the words’ local contexts extracted from the text snippets returned by searching in Google. The statistical and semantic measures are further combined into an improved algorithm for identification of false friends that achieves almost twice better results than previously known algorithms. The evaluation is performed for identifying cognates between Bulgarian and Russian but the proposed methods could be adopted for other language pairs for which parallel corpora and bilingual glossaries are available

Term Extraction and Disambiguation for Semantic Knowledge Enrichment: A Case Study on Initial Public Offering (IPO)

Domain knowledge bases are a basis for advanced knowledge-based systems, manually creating a formal knowledge base for a certain domain is both resource consuming and non-trivial. In this paper, we propose an approach that provides support to extract, select, and disambiguate terms embedded in domain specific documents. The extracted terms are later used to enrich existing ontologies/taxonomies, as well as to bridge domain specific knowledge base with a generic knowledge base such as WordNet. The proposed approach addresses two major issues in the term extraction domain, namely quality and efficiency. Also, the proposed approach adopts a feature-based method that assists in topic extraction and integration with existing ontologies in the given domain. The proposed approach is realized in a research prototype, and then a case study is conducted in order to illustrate the feasibility and the efficiency of the proposed method in the finance domain. A preliminary empirical validation by the domain experts is also conducted to determine the accuracy of the proposed approach. The results from the case study indicate the advantages and potential of the proposed approach

Drug-drug interactions: A machine learning approach

Automatic detection of drug-drug interaction (DDI) is a difficult problem in pharmaco-surveillance. Recent practice for in vitro and in vivo pharmacokinetic drug-drug interaction studies have been based on carefully selected drug characteristics such as their pharmacological effects, and on drug-target networks, in order to identify and comprehend anomalies in a drug\u27s biochemical function upon co-administration.;In this work, we present a novel DDI prediction framework that combines several drug-attribute similarity measures to construct a feature space from which we train three machine learning algorithms: Support Vector Machine (SVM), J48 Decision Tree and K-Nearest Neighbor (KNN) using a partially supervised classification algorithm called Positive Unlabeled Learning (PU-Learning) tailored specifically to suit our framework.;In summary, we extracted 1,300 U.S. Food and Drug Administration-approved pharmaceutical drugs and paired them to create 1,688,700 feature vectors. Out of 397 drug-pairs known to interact prior to our experiments, our system was able to correctly identify 80% of them and from the remaining 1,688,303 pairs for which no interaction had been determined, we were able to predict 181 potential DDIs with confidence levels greater than 97%. The latter is a set of DDIs unrecognized by our source of ground truth at the time of study.;Evaluation of the effectiveness of our system involved querying the U.S. Food and Drug Administration\u27s Adverse Effect Reporting System (AERS) database for cases involving drug-pairs used in this study. The results returned from the query listed incidents reported for a number of patients, some of whom had experienced severe adverse reactions leading to outcomes such as prolonged hospitalization, diminished medicinal effect of one or more drugs, and in some cases, death

Three Essays on Enhancing Clinical Trial Subject Recruitment Using Natural Language Processing and Text Mining

Patient recruitment and enrollment are critical factors for a successful clinical trial; however, recruitment tends to be the most common problem in most clinical trials. The success of a clinical trial depends on efficiently recruiting suitable patients to conduct the trial. Every clinical trial research has a protocol, which describes what will be done in the study and how it will be conducted. Also, the protocol ensures the safety of the trial subjects and the integrity of the data collected. The eligibility criteria section of clinical trial protocols is important because it specifies the necessary conditions that participants have to satisfy. Since clinical trial eligibility criteria are usually written in free text form, they are not computer interpretable. To automate the analysis of the eligibility criteria, it is therefore necessary to transform those criteria into a computer-interpretable format. Unstructured format of eligibility criteria additionally create search efficiency issues. Thus, searching and selecting appropriate clinical trials for a patient from relatively large number of available trials is a complex task. A few attempts have been made to automate the matching process between patients and clinical trials. However, those attempts have not fully integrated the entire matching process and have not exploited the state-of-the-art Natural Language Processing (NLP) techniques that may improve the matching performance. Given the importance of patient recruitment in clinical trial research, the objective of this research is to automate the matching process using NLP and text mining techniques and, thereby, improve the efficiency and effectiveness of the recruitment process. This dissertation research, which comprises three essays, investigates the issues of clinical trial subject recruitment using state-of-the-art NLP and text mining techniques. Essay 1: Building a Domain-Specific Lexicon for Clinical Trial Subject Eligibility Analysis Essay 2: Clustering Clinical Trials Using Semantic-Based Feature Expansion Essay 3: An Automatic Matching Process of Clinical Trial Subject Recruitment In essay1, I develop a domain-specific lexicon for n-gram Named Entity Recognition (NER) in the breast cancer domain. The domain-specific dictionary is used for selection and reduction of n-gram features in clustering in eassy2. The domain-specific dictionary was evaluated by comparing it with Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT). The results showed that it add significant number of new terms which is very useful in effective natural language processing In essay 2, I explore the clustering of similar clinical trials using the domain-specific lexicon and term expansion using synonym from the Unified Medical Language System (UMLS). I generate word n-gram features and modify the features with the domain-specific dictionary matching process. In order to resolve semantic ambiguity, a semantic-based feature expansion technique using UMLS is applied. A hierarchical agglomerative clustering algorithm is used to generate clinical trial clusters. The focus is on summarization of clinical trial information in order to enhance trial search efficiency. Finally, in essay 3, I investigate an automatic matching process of clinical trial clusters and patient medical records. The patient records collected from a prior study were used to test our approach. The patient records were pre-processed by tokenization and lemmatization. The pre-processed patient information were then further enhanced by matching with breast cancer custom dictionary described in essay 1 and semantic feature expansion using UMLS Metathesaurus. Finally, I matched the patient record with clinical trial clusters to select the best matched cluster(s) and then with trials within the clusters. The matching results were evaluated by internal expert as well as external medical expert