Digging in the Dirt: Extracting Keyphrases from Texts with KD
Authors: Giovanni Moretti, Rachele Sprugnoli, Sara Tonelli (Fondazione Bruno Kessler, Università di Trento, 2015)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this paper we present a keyphrase extraction system called Keyphrase Digger (KD). The tool uses both statistical measures and linguistic information to detect a weighted list of n-grams representing the most important concepts of a text. KD is the reimplementation of an existing tool, which has been extended with new features, a high level of customizability, a shorter processing time and an extensive evaluation on different text genres in English and Italian (i.e. scientific articles and historical texts).

Introduction
This paper presents Keyphrase Digger (henceforth KD), a new implementation of the KX system for keyphrase extraction. Both KX (Pianta and Tonelli, 2010; Tonelli et al., 2012) and KD combine statistical measures with linguistic information to identify and extract weighted keyphrases from English and Italian texts. KX took part to the SemEval 2010 task on “Automatic Keyphrase Extraction from Scientific Articles” (Kim et al., 2010) achieving the 7th best result out of 20 in the final ranking. KX is part of TextPro (Pianta et al., 2008), a suite of NLP tools developed by Fondazione Bruno Kessler 1 . The aim of KX re-implementation was to improve system performance in terms of F-measure, processing speed and customizability, so to make its integration possible in web-based applications. Besides, its adaptation to different types of texts has become possible also for not expert users, and its application to large document collections has been significantly improved. Keyphrases are n-grams of different length, both single and multi-token expressions, which capture the main concepts of a given document (Turney, 2000). Their extraction is very useful when integrated in complex NLP tasks such as text categorization (Hulth and Megyesi, 2006), opinion mining (Berend, 2011) and summarization (D’Avanzo and Magnini, 2005)2 . Moreover keyphrases, especially if displayed using an effective visualization, can help summarize and navigate document collections so to allow their socalled ‘distant reading’ (Moretti, 2013). This need to easily grasp the concise content of a text through keyphrases is particularly relevant given the increasing availability of digital document collections in many domains. Nevertheless, outside the computational linguistics community, for example among humanities scholars, the extraction of keywords is often assimilated with the extraction of the most frequent (single) words in a text, see for instance the success of tools such as Textal3 and Voyant4 in the Digital Humanities community. In some cases, stopwords are still included among the top-ranked keywords, leading to a key-concept list which is little informative and just reflects the Zipfian distribution of words in language. KD, instead, is designed to be easily customized by scholars from different communities, while sticking to the definition of keyphrases in use in the computational linguistics community. The remainder of the paper is structured as follows. In Section 2 we describe KD architecture and features while in Section 3 the system evaluation is reported. We present the application of KX to the historical domain and the web-based interface available online in Section 4. Future works and conclusions are drawn in Section 5.

Conclusion
This paper presents KD, a keyphrase extraction system that re-implements the basic algorithm of KX but adds new features, a high level of customizability and an improved processing speed. KD currently works on English and Italian and can take in input texts pre-processed with different available PoS taggers and lemmatizers for these two languages. Nevertheless, the system could be easily adapted to manage more languages and additional PoS taggers by modifying few configuration parameters. KD will be soon integrated in the next TextPro release12 and it will be also released as a standalone module. Meanwhile, we made it available online as part of an easy-to-use web application, so that it can be easily accessed also by users without a technical background. This work targets in particular humanities scholars, who often do not know how to access state-of-the-art tools for keyphrase extraction.