Elnaz Davoodi

An Investigation on the Influence of Genres and Textual Organisation on the Use of Discourse Relations

In this paper, we investigate some of the problems associated with the automatic extraction of discourse relations. In particular, we study the influence of communicative goals encoded in a given genre against another, and between the various communicative goals encoded between sections of documents of a same genre. Some investigations have been made in the past in order to identify the differences seen across either genres or textual organization, but none have made a thorough statistical analysis of these differences across currently available annotated corpora. In this paper, we show that both the communicative goal of a given genre and, to a lesser extend, that of a particular topic tackled by that genre, do in fact influence in the distribution of discourse relations. Using a statistically grounded approach, we show that certain discourse relations are more likely to appear within given genres and subsequently within sections within a genre. In particular, we observed that Attributions are common in the newspaper articles genre while Joint relations are comparatively more frequent in online reviews. We also notice that Temporal relations are statically more common in the methodology sections of scientific research documents than in the rest of the text. These results are important as they give clues to allow the tailoring of current discourse taggers to specific textual genres.

On the Contribution of Discourse Structure on Text Complexity Assessment.

This paper investigates the influence of discourse features on text complexity assessment. To do so, we created two data sets based on the Penn Discourse Treebank and the Simple English Wikipedia corpora and compared the influence of coherence, cohesion, surface, lexical and syntactic features to assess text complexity. Results show that with both data sets coherence features are more correlated to text complexity than the other types of features. In addition, feature selection revealed that with both data sets the top most discriminating feature is a coherence feature.

On the Usage of Discourse Relations Across Texts with Different Readability Levels

In a coherent text, text spans are not understood in isolation but in relation with each other through discourse relations, such as Cause, Condition, Elaboration, etc. Discourse analysis involves modeling the coherence relations between text segments which allows readers to interpret and understand the communicative purpose of text’s constitutive segments. Many natural language processing applications such as text summarization, question answering, text simplification, etc. can benefit from discourse analysis. In the proposed research project, we plan to use discourse analysis in the context of text simplification in order to enhance a text’s readability level.

Semantic Mapping of Security Events to Known Attack Patterns

In order to provide cyber environment security, analysts need to analyze a large number of security events on a daily basis and take proper actions to alert their clients of potential threats. The increasing cyber traffic drives a need for a system to assist security analysts to relate security events to known attack patterns. This paper describes the enhancement of an existing Intrusion Detection System (IDS) with the automatic mapping of snort alert messages to known attack patterns. The approach relies on pre-clustering snort messages before computing their similarity to known attack patterns in Common Attack Pattern Enumeration and Classification (CAPEC). The system has been deployed in our partner company and when evaluated against the recommendations of two security analysts, achieved an f-measure of 64.57%.

A Context-Aware Approach for the Identification of Complex Words in Natural Language Texts

This paper evaluates the effect of the context on the identification of complex words in natural language texts. The approach automatically tags words as either complex or not, based on two sets of features: base features that only pertain to the target word, and contextual features that take the context of the target word into account. We experimented with several supervised machine learning models, and trained and tested the approach with the SemEval-2016 dataset. Results show that considering contextual features significantly improves the identification of complex words by reaching an F-measure of 0.260 compared to 0.184 without them.

On the Influence of Contextual Features for the Identification of Complex Words

This paper evaluates the effect of the context of a target word on the identification of complex words in natural language texts. The approach automatically tags words as either complex or not, bas...

Classification of Textual Genres Using Discourse Information

This papers aims to measure the influence of textual genre on the usage of discourse relations and discourse markers. Specifically, we wish to evaluate to what extend the use of certain discourse relations and discourse markers are correlated to textual genre and consequently can be used to predict textual genre. To do so, we have used the British National Corpus and compared a variety of discourse-level features on the task of genre classification.