Identifying Predictive Features for Textual Genre Classification: ,the Key Role of Syntax
Authors: Andrea Cimino, Martijn Wieling, Felice Dell'Orletta, Simonetta Montemagni, Giulia Venturi (University of Groningen, CNR-ILC, 2017)
Topic: Textual Genres & Literature Linguistics

Abstract
The paper investigates impact and role of different feature types for the specific task of Automatic Genre Classification with the final aim of identifying the most predictive ones. The goal was pursued by carrying out incremental feature selection through Grafting using different sets %(word-level vs multi-level) of linguistic features. Achieved results for discriminating among four traditional textual genres show the key role played by syntactic features, whose impact turned out to vary across genres.

Introduction
Automatic classification of textual genres has always received significant attention from both theoretical and application perspectives. On the one hand, it has been considered relevant by linguists and educators to teach students the correct way of writing in specific communicative scenarios AUTHOR. On the other hand, the classification of textual genres is seen as a way to cope with the well known problem of information overload: the exploitation of information about document genre can help to develop more accurate Information Retrieval tools. Genre identification has been considered a key factor for reducing irrelevant %inadequate results of search engines, as users would be able to specify the desired textual genre along with the keywords expressing the content they are looking for AUTHOR. In fact, document genre and document content represent orthogonal dimensions of classifications AUTHOR. A variety of different approaches to Automatic Genre Classification (AGC) has been proposed so far differing at the level of the genre and the typology of features considered. According to the widely acknowledged fact that no established classification of genres exists (see e.g. AUTHOR or AUTHOR), previous studies focused on `traditional genres' such as journalism, handbooks, academic prose, see among the others AUTHOR, and on `web genres', i.e.\ genres of web pages, see e.g. AUTHOR. Despite the great interest in the investigation of which linguistic features qualify a text genre AUTHOR, so far little effort has been devoted to use sophisticated NLP techniques, such as %dependency syntactic parsing, to capture complex linguistic features for the automatic classification of textual genres. Differently from other application scenarios where the form (the style) of a document is investigated, such as e.g. Authorship Attribution AUTHOR, Readability Assessment AUTHOR and Native Language Identification AUTHOR, AGC approaches proposed so far mainly focus on word level linguistic features, in particular the distribution of function words, word frequency, n--gram models of both characters and Parts--Of-Speech AUTHOR or finer-grained Parts--Of-Speech tags including morpho-syntactic features such as verb tense AUTHOR. Very few studies rely on features extracted from syntactically annotated texts, the exception being AUTHOR who combines lexical features (i.e.\ word frequency) with features extracted from the output of a chunk boundary detector (e.g. the distribution of noun, verbal, adjectival phrases), the average number of words included in verbal phrases. Similar structural features have been also used by AUTHOR who combined web--specific features (e.g.\ HTML tags) with lexical information and features aiming at capturing the syntactic structure of a sentence, e.g. the distribution of declarative and imperative sentences, syntactic ambiguities, etc. In this paper, we tackle the AGC task for traditional genres (namely literary, scientific, educational and journalistic texts) by using different types of linguistic features, i.e. lexical, morpho-syntactic and syntactic. In particular, the following research questions are addressed: i) which are the most effective features to classify a textual genre, and ii) whether and to what extent features identified as most effective remain the same across different genres. These questions have been addressed by carrying out incremental feature selection with the final aim of identifying the most predictive ones. So far, studies focused on the best set of features to classify textual genres have been carried out mainly on English. In this paper, this issue is investigated for a typologically different language, Italian.

Conclusion
"In this paper we investigated impact and role of different feature types for Automatic Genre Classification. The goal was pursued by carrying out incremental feature selection through Grafting augmented with TinyEst. Two sets of features were taken into account, token-based and structure-based. Achieved results %for discriminating among four traditional textual genres show the key role played by syntactic features, % with respect to token-based ones: a result %, possibly influenced by the language dealt with (Italian), which is new with respect to the AGC literature. Another original contribution is concerned with the role of %Interestingly, the contribution of different feature types which turned out to vary across textual genres, suggesting the specialization of features %to be taken into account in binary genre classification tasks (e.g. Literature vs. other genres%not-Literature ). The features contributing to AGC for Italian are possibly influenced by the language dealt with. Although it is widely acknowledged that linguistic variation across genres is a language universal, the question is whether similar linguistic features are expected to play a similar role across languages. If this might be the case of features such as e.g. TTR, use of passive voice, tenses or pronouns, on the other hand features concerned with the ordering of sentence constituents or the overall sentence structure (e.g. parse tree depth or dependency length) may be distinctive to a specific language or language family. Further directions of research thus include comparison of results in a multilingual perspective as well as across a wider variety of genres.