Daniela Gîfu

Multi-Dimensional Analysis of Political Language

This paper presents a method for the valuation of discourses from different linguistic perspectives: lexical, syntactic and semantic. We describe a platform discourse analysis tool (DAT) which integrates a range of language processing tools with the intent to build complex characterisations of the political discourse. The idea behind this construction is that the vocabulary and the clause structure of the sentence betray the speaker’s level of culture, while the semantic classes mentioned in a speech characterises the speaker’s orientation. When the object of study is the political discourse, an investigation on these dimensions could put in evidence features influencing the electing public. The method is intended to help both political speakers to improve their discourse abilities, by comparing their speeches with those of personalities of the public life in the past, and the public at large by evidencing hidden aspects of the linguistic and intellectual abilities of their candidates.

Computational Techniques in Political Language Processing: AnaDiP-2011

This paper presents a computational method, AnaDiP-2011, based on natural language processing (NLP) techniques for the interpretation of the political discourse. The application considers the 2009 presidential campaign in Romania. The concept behind this method is that the manner in which individuals speak and write betrays their sensibility. Our investigation is intended to give support to researchers, specialists in political sciences, political analysts and election’s staff, being helpful mainly in their social exploration of the electoral campaigns in their intend to measure reactions with respect to the developments in the political scene.

Machine Learning and Creative Methods Used to Classify Customers in a CRM Systems

Considering the fact that business processes are becoming more and more customer-oriented, marketing and connecting top management to the customers are extremely important, which should be given a special attention, taking into account the globalization and the increasing market competition. Due to the exponential growth of data volume, in any field, it was necessary to use and develop new methods and techniques for discovering hidden information in data, information almost impossible to be detected by traditional means, based on human analysis skills. This paper presents a pre-processing method for mining data in order to support decision makers in developing coherent and saleable strategies as regards the customers, based on their classification in different categories, specific to the area of concern, for the purpose of improving the CRM systems. In order to facilitate analysis and decision making by top management, the analyzed results are presented - in graphic format-using the facilities provided by Google API integrated in the application created with Open Source technologies.

Quo Vadis: A Corpus of Entities and Relations

This chapter describes a collective work aimed to build a corpus including annotations of semantic relations on a text belonging to the belletristic genre. The paper presents conventions of annotations for four categories of semantic relations and the process of building the corpus as a collaborative work. Part of the annotation is done automatically, such as the token/part of speech/lemma layer, and is performed during a preprocessing phase. Then, an entity layer (where entities of type person are marked) and a relation layer (evidencing binary relations between entities) are added manually by a team of trained annotators, the result being a heavily annotated file. A number of methods to obtain accuracy are detailed. Finally, some statistics over the corpus are drawn. The language under investigation is Romanian, but the proposed annotation conventions and methodological hints are applicable to any language and text genre.

Time Evolution of Writing Styles in Romanian Language

This paper presents a diachronic analysis centered on the exploration of differences between the writing styles of journalistic texts in Romanian language. This analysis is focused on the time evolution of this language across two adjacent regions, Bessarabia and Romania in two major periods that were marked by important historical differences. Our aim is to examine these language differences based on corpora of historical and contemporary texts. To this end, we employ the ReaderBench framework to calculate a number of textual complexity indices that can be reliably used to characterize writing style. These analyses are conducted on two independent corpora for each of the two language styles, covering the following time periods: 1941-1991, when Bessarabia was separated from Romania and became a state in the Soviet Union (and there were few connections and language influences with Romania), and after July 1991, when Bessarabia became an independent state, Republic of Moldavia (and many language interactions with Romania occurred). The results of our analyses highlight the lexical and cohesive textual complexity indices that best reflect the differences in writing style, ranging from sentence and paragraph structure to word entropy and cohesion, measured in terms of Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA).

A Mixed Approach in Recognising Geographical Entities in Texts

The paper describes an approach for automatic identification in Romanian texts of name entities belonging to the geographical domain. The research is part of a project (MappingBooks) aimed to link mentions of entities in an e-book with external information, as found in social media, Wikipedia, or web pages containing cultural or touristic information, in order to enhance the reader’s experience. The described name entity recognizer mixes ontological information, as found in public resources, with handwritten symbolic rules. The outputs of the two component modules are compared and heuristics are used to take decisions in cases of conflict.

Computational Techniques In Management Of Engineering And Business Institutions

Abstract This paper deals with computational techniques used in management engineering in order to support enterprise managers in the decision-making process. Thus, the paper presents an application, built with web technologies for extracting and interpreting information from various sources, enabling the user to analyze data both in text files and the data available on the Internet, results that greatly improves the decision-making process through an efficient and fast analysis of data which, due to large the volume growing exponentially can no longer be covered and analyzed “manually” by a human factor.

The Curative Power of Medical Data

In an era when massive amounts of medical data became available, researchers working in biological, biomedical and clinical domains have increasingly started to require the help of language engineers to process large quantities of biomedical and molecular biology literature, patient data or health records. With such a huge amount of reports, evaluating their impact has long seized to be a trivial task. Linking the contents of these documents to each other, as well as to specialized ontologies, could enable access to and discovery of structured clinical information and foster a major leap in natural language processing and health research

Research on agricultural monitoring system based on convolutional neural network

Abstract With the rapid development of social media, fluctuations in the price of vegetables are passed on to the people through the Internet in real time, which will certainly attract widespread attention in China. Therefore, Public opinion in social media is regarded as a latent factor contributing to market fluctuation. To predict the vegetable price fluctuation in China’s market, a hybrid prediction model combining convolution neural network with corpora is constructed. Although a direct causality test shows the uncertainty between the vegetable price and public opinion in social media, strong causality is found after removing the seasonal effect of price. This shows that the spread of public opinion through the Internet can strengthen the link between vegetable price changes and external events by affecting the expectations of market traders.

Detecting Bridge Anaphora in Novels

The paper presents the importance of a proper understanding of the novel content trough the perspective of bridge relationship type. Furthermore, we are interested in developing an instrument that can automatically identify the semantic bridge relationships for any sort of text. In this regard, we also suggest the automatic recognizing of anaphoric relationships with a high precision. This can be possible using a manually annotated collection of texts (Quo Vadis novel), used as training data. This study is intended to help direct beneficiaries, such as specialists and researchers in the field of natural language processing, linguists, psychologists, sociologists, economists, etc.