Computer Support of Semantic Text Analysis of a Technical Specification on Designing Software

The given work is devoted to development of the computer-aided system of semantic text analysis of a technical specification. The purpose of this work is to increase efficiency of software engineering based on automation of semantic text analysis of a technical specification. In work it is offered and investigated a technique of the text analysis of a technical specification is submitted, the expanded fuzzy attribute grammar of a technical specification, intended for formalization of limited Russian language is constructed with the purpose of analysis of offers of text of a technical specification, style features of the technical specification as class of documents are considered, recommendations on preparation of text of a technical specification for the automated processing are formulated. The computer-aided system of semantic text analysis of a technical specification is considered. This system consist of the following subsystems: preliminary text processing, the syntactic and semantic analysis and construction of software models, storage of documents and interface.

К Анализу Естественно-Языковых Объектов

Рассматриваются проблемы анализа естественно-языковых объектов (ЕЯО) с точки зрения их представления и обработки в памяти компьютера. Предложена формализация задачи анализа ЕЯО и приведен пример формализованного представления ЕЯО предметной области.

Обработка Предложений Естественного Языка с Использованием Словарей и Частоты Появления Слов

Описывается один из подходов к анализу естественно-языкового текста, который использует толковый словарь естественного языка, локальный словарь анализируемого текста и частотные характеристики слов в этом тексте.

Cultural Knowledge for Named Entity Disambiguation: A Graph-Based Semantic Relatedness Approach

One of the ultimate aims of Natural Language Processing is to automate the analysis of the meaning of text. A fundamental step in that direction consists in enabling effective ways to automatically link textual references to their referents, that is, real world objects. The work presented in this paper addresses the problem of attributing a sense to proper names in a given text, i.e., automatically associating words representing Named Entities with their referents. The method for Named Entity Disambiguation proposed here is based on the concept of semantic relatedness, which in this work is obtained via a graph-based model over Wikipedia. We show that, without building the traditional bag of words representation of the text, but instead only considering named entities within the text, the proposed method achieves results competitive with the state-of-the-art on two different datasets.

A rule-based approach to implicit emotion detection in text

Most research in the area of emotion detection in written text focused on detecting explicit expressions of emotions in text. In this paper, we present a rule-based pipeline approach for detecting implicit emotions in written text without emotion-bearing words based on the OCC Model. We have evaluated our approach on three different datasets with five emotion categories. Our results show that the proposed approach outperforms the lexicon matching method consistently across all the three datasets by a large margin of 17–30% in F-measure and gives competitive performance compared to a supervised classifier. In particular, when dealing with formal text which follows grammatical rules strictly, our approach gives an average F-measure of 82.7% on “Happy”, “Angry-Disgust” and “Sad”, even outperforming the supervised baseline by nearly 17% in F-measure. Our preliminary results show the feasibility of the approach for the task of implicit emotion detection in written text.

An unsupervised Bayesian modelling approach to storyline detection from news articles

Storyline detection from news articles aims at summarizing events described under a certain news topic and revealing how those events evolve over time. It is a difficult task because it requires first the detection of events from news articles published in different time periods and then the construction of storylines by linking events into coherent news stories. Moreover, each storyline has different hierarchical structures which are dependent across epochs. Existing approaches often ignore the dependency of hierarchical structures in storyline generation. In this paper, we propose an unsupervised Bayesian model, called dynamic storyline detection model, to extract structured representations and evolution patterns of storylines. The proposed model is evaluated on a large scale news corpus. Experimental results show that our proposed model outperforms several baseline approaches.

Análise sintagmática aplicada ao processo de sumarização automática de documentos do português brasileiro

This research studies the application of syntagmatic analysis of written texts in the language of Brazilian Portuguese as a methodology for the automatic creation of extractive summaries. The automation of abstracts, while linked to the area of natural language processing (PLN) is studying ways the computer can autonomously construct summaries of texts. For this we use as presupposed the idea that switch to the computer the way a language is structured, in our case the Brazilian Portuguese, it will help in the discovery of the most relevant sentences, and consequently build extractive summaries with higher informativeness. In this study, we propose the definition of a summarization method that automatically perform the syntagmatic analysis of texts and through them, to build an automatic summary. The phrases that make up the syntactic structures are then used to analyze the sentences of the text, so the count of these elements determines whether or not a sentence will compose the summary to be generated

Parsing mathematical constructs:results from a preliminary eye tracking study

The semantic model described in this paper is based on ones developed for arithmetic (e.g. McCloskey et al. 1985, Cohene and Dehaene 1995), natural language processing (Fodor 1975, Chomsky 1981) and work by the author on how learners parse mathematical structures. The semantic model highlights the importance of the parsing process and the relationship between this process and the mathematical lexicon/grammar. It concludes by demonstrating that for a learner to become an efficient, competent mathematician a process of top-down parsing is essential.

The development of a semantic model for the interpretation of mathematics including the use of technology

The semantic model developed in this research was in response to the difficulty a group of mathematics learners had with conventional mathematical language and their interpretation of mathematical constructs. In order to develop the model ideas from linguistics, psycholinguistics, cognitive psychology, formal languages and natural language processing were investigated. This investigation led to the identification of four main processes: the parsing process, syntactic processing, semantic processing and conceptual processing. The model showed the complex interdependency between these four processes and provided a theoretical framework in which the behaviour of the mathematics learner could be analysed. The model was then extended to include the use of technological artefacts into the learning process. To facilitate this aspect of the research, the theory of instrumentation was incorporated into the semantic model. The conclusion of this research was that although the cognitive processes were interdependent, they could develop at different rates until mastery of a topic was achieved. It also found that the introduction of a technological artefact into the learning environment introduced another layer of complexity, both in terms of the learning process and the underlying relationship between the four cognitive processes.

Recommending peers for learning: Matching on dissimilarity in interpretations to provoke breakdown

People recommenders are a widespread feature of social networking sites and educational social learning platforms alike. However, when these systems are used to extend learners’ Personal Learning Networks, they often fall short of providing recommendations of learning value to their users. This paper proposes a design of a people recommender based on content-based user profiles, and a matching method based on dissimilarity therein. It presents the results of an experiment conducted with curators of the content curation site Scoop.it!, where curators rated personalized recommendations for contacts. The study showed that matching dissimilarity of interpretations of shared interests is more successful in providing positive experiences of breakdown for the curator than is matching on similarity. The main conclusion of this paper is that people recommenders should aim to trigger constructive experiences of breakdown for their users, as the prospect and potential of such experiences encourage learners to connect to their recommended peers.

Modeling Individual Differences among Writers Using ReaderBench

The current study builds upon a previous study, which examined the degree to which the lexical properties of students’ essays could predict their vocabulary scores. We expand on this previous research by incorporating new natural language processing indices related to both the surface- and discourse-levels of students’ essays. Additionally, we investigate the degree to which these NLP indices can be used to account for variance in students’ reading comprehension skills. We calculated linguistic essay features using our framework, ReaderBench, which is an automated text analysis tools that calculates indices related to linguistic and rhetorical features of text. University students (n = 108) produced timed (25 minutes), argumentative essays, which were then analyzed by ReaderBench. Additionally, they completed the Gates-MacGinitie Vocabulary and Reading comprehension tests. The results of this study indicated that two indices were able to account for 32.4% of the variance in vocabulary scores and 31.6% of the variance in reading comprehension scores. Follow-up analyses revealed that these models further improved when only considering essays that contained multiple paragraph (R2 values = .61 and .49, respectively). Overall, the results of the current study suggest that natural language processing techniques can help to inform models of individual differences among student writers.

Document Cohesion Flow: Striving towards Coherence

Text cohesion is an important element of discourse processing. This paper presents a new approach to modeling, quantifying, and visualizing text cohesion using automated cohesion flow indices that capture semantic links among paragraphs. Cohesion flow is calculated by applying Cohesion Network Analysis, a combination of semantic distances, Latent Semantic Analysis, and Latent Dirichlet Allocation, as well as Social Network Analysis. Experiments performed on 315 timed essays indicated that cohesion flow indices are significantly correlated with human ratings of text coherence and essay quality. Visualizations of the global cohesion indices are also included to support a more facile understanding of how cohesion flow impacts coherence in terms of semantic dependencies between paragraphs.

(Semi-)automatisch ondertitelen en vertalen van leermateriaal

This presentation summarizes experience with the automated speech recognition and translation approach realised in the context of the European project EMMA.

Classifying Written Texts Through Rhythmic Features

Rhythm analysis of written texts focuses on literary analysis and it mainly considers poetry. In this paper we investigate the relevance of rhythmic features for categorizing texts in prosaic form pertaining to different genres. Our contribution is threefold. First, we define a set of rhythmic features for written texts. Second, we extract these features from three corpora, of speeches, essays, and newspaper articles. Third, we perform feature selection by means of statistical analyses, and determine a subset of features which efficiently discriminates between the three genres. We find that using as little as eight rhythmic features, documents can be adequately assigned to a given genre with an accuracy of around 80 %, significantly higher than the 33 % baseline which results from random assignment.

Expressing Sentiments in Game Reviews

Opinion mining and sentiment analysis are important research areas of Natural Language Processing (NLP) tools and have become viable alternatives for automatically extracting the affective information found in texts. Our aim is to build an NLP model to analyze gamers’ sentiments and opinions expressed in a corpus of 9750 game reviews. A Principal Component Analysis using sentiment analysis features explained 51.2 % of the variance of the reviews and provides an integrated view of the major sentiment and topic related dimensions expressed in game reviews. A Discriminant Function Analysis based on the emerging components classified game reviews into positive, neutral and negative ratings with a 55 % accuracy.