Stella Markantonatou

APPLYING THE SOM MODEL TO TEXT CLASSIFICATION ACCORDING TO REGISTER AND STYLISTIC CONTENT

We report on the application of the Self-Organizing Map (SOM) classification method to the task of categorizing texts according to their register and the style of their author. The SOM has been selected as its performance in various data-mining applications has been found to be highly successful. Here, the method is evaluated against the task of clustering textual data which are corpora of texts written in the Greek language; the parameters used depict linguistically important structural properties of the texts. The experiments reported indicate that the SOM results are equivalent to those generated by statistical methods.

Discriminating the Registers and Styles in the Modern Greek Language-Part 2: Extending the Feature Vector to Optimize Author Discrimination

This article describes a method for discriminating among authors within a given register of Modern Greek. The focus here is to determine to what extent the stylistic differences among authors can be detected with a high degree of accuracy for a set of texts belonging to a well-defined register. To that end, the chosen register is characterized by a well-defined sub-language, from which a corpus of more than 1,000 documents has been created. To discriminate the texts according to author style, a series of experiments have been performed using statistical techniques. Each text has been represented by a vector covering several linguistic aspects, in an effort to determine the most effective style markers. The experimental results indicate that the proposed approach can successfully separate the author styles for a given register. An extensive study of the effectiveness of the different variable categories has been performed. For instance, diglossia information on its own is not sufficient for author discrimination. Instead, a systematic evaluation process indicates that part-of-speech, structural and algorithmically derived lemma-frequency variables are the most important style markers, their use leading to an author discrimination accuracy exceeding 90%.

Modern Greek Deverbal Nominals: An LMT Approach.

This paper argues that there are Modern Greek (MG) deverbal nominal predicates which take syntactic arguments. A mechanism for the derivation of these nominals requiring the existence of an ‘internal’ ([ — r]) argument is proposed which has broader coverage than the mechanism proposed in Grimshaw (1990). A small set of simple, unification-based operations is employed to model the relation between the argument structure of verb predicates and that of the corresponding deverbal nominals with an ‘ eventive’ reading. The Lexical Mapping Theory of LFG (LMT) is the theoretical framework of the present discussion.

METIS-II: low resource machine translation

METIS-II was an EU-FET MT project running from October 2004 to September 2007, which aimed at translating free text input without resorting to parallel corpora. The idea was to use “basic” linguistic tools and representations and to link them with patterns and statistics from the monolingual target-language corpus. The METIS-II project has four partners, translating from their “home” languages Greek, Dutch, German, and Spanish into English. The paper outlines the basic ideas of the project, their implementation, the resources used, and the results obtained. It also gives examples of how METIS-II has continued beyond its lifetime and the original scope of the project. On the basis of the results and experiences obtained, we believe that the approach is promising and offers the potential for development in various directions.

Discriminating the registers and styles in the Modern Greek language

This article investigates (a) whether register discrimination can successfully exploit linguistic information reflecting the evolution of a language (such as the diglossia phenomenon of the Modern Greek language) and (b) what kind of linguistic information and which statistical techniques may be employed to distinguish among individual styles within one register. Using clustering techniques and features reflecting the diglossia phenomenon, we have successfully discriminated registers in Modern Greek. However, diglossia information has not been shown sufficient to distinguish among individual styles within one register. Instead, a large number of linguistic features need to be studied with methods such as discriminant analysis in order to obtain a high degree of discrimination accuracy.

Lexical rules: what are they?

Horizontal redundancy is inherent to lexica consisting of descriptions of fully formed objects. This causes an unwelcome expansion of the lexical database and increases parsing time. To climinate it, direct relations between descriptions of fully formed objects are often defined. These are additional to the (Typed Multiple) Inheritance Network which already structures the lexicon. Many implementations of horizontal relations, however, fail to generate lexical entries on a needs-driven basis, so eliminate neither the problem of lexicon expansion nor that of inefficient parsing. Alternatively, we propose that lexical entries are descriptions of objects open to contextual specification of their properties on the basis of constraints defined within the type system. This guarantees that only those grammatical lexical entries are infered that are needed for efficient parsing. The proposal is extremely modest, making use of only basic inference power and expressivity.

Evaluating SOM-based Models in Text Classification Tasks for the Greek Language

In the present paper, the Self-Organising Map (SOM) is applied to the problem of categorising a corpus of Modem Greek texts according to the style of their authors. A number of variants of the SOM model are used in a series of experiments, in order to compare and contrast their behaviour in the specific task. The experimental results indicate that the SOM possesses the ability to analyse such data, successfully uncovering the differences among authors.