KD Strikes Back: from Keyphrases to Labelled Domains Using External Knowledge Sources
Authors: Giovanni Moretti, Rachele Sprugnoli, Sara Tonelli (Fondazione Bruno Kessler, Università di Trento, 2016)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper presents L-KD, a tool that relies on available linguistic and knowledge resources to perform keyphrase clustering and labelling. The aim of L-KD is to help finding and tracing themes in English and Italian text data, represented by groups of keyphrases and associated domains. We perform an evaluation of the top-ranked domains using the 20 Newsgroup dataset, and we show that 8 domains out of 10 match with manually assigned labels. This confirms the good accuracy of this approach, which does not require supervision.

Introduction
With the increasing availability of large document collections in digital format, companies, organizations but also non-expert users face everyday the need to efficiently extract and categorize relevant information from large corpora. The possibility to extract key-concepts and assign them to a domain without the need of supervision would allow them to systematically track the flow of information and retain only relevant content at two granularity levels: key-concepts, and domains to which these key-concepts can be ascribed. Although topic models AUTHOR can be used to this purpose, they have two main drawbacks: the number of topics for a corpus is arbitrary and topics are often not labelled. \\ In this work, we present a solution to the aforementioned research problem by presenting L-KD (Labelled-KD), a tool to perform keyphrase clustering and labelling through the exploitation of external linguistic and knowledge resources. The tool takes advantage of the availability of Keyphrase Digger} (KD), a multilingual rule-based system that detects a weighted list of n-grams representing the most important concepts in a text AUTHOR. These key-concepts are then linked to WordNet Domains AUTHOR in order to create clusters of key-concepts labelled by domain. The problem of ambiguous concepts, i.e. possibly belonging to more than one WordNet domain, is tackled by using ConceptNet 5 AUTHOR, a multilingual knowledge source containing single and multi-word concepts linked to each other by a broad set of relations covering different types of associations. The outcome of this study is the L-KD tool, supporting both English and Italian, which we make available to the research community}. L-KD takes in input a document in plain text format, and outputs the ranked list of semantic domains discussed in the documents, each associated with a set of keyphrases.

Conclusion
This paper presents L-KD, a tool that extracts keyphrases from text data, clusters them according to the domain and assigns a label to each cluster. The process underlying L-KD is based on the exploitation of external linguistic and knowledge resources, i.e. WordNet Domains and ConceptNet 5. Our tool can process both English and Italian texts of different length and content, from a single news article to an entire book, from single-theme to multi-theme documents. In the future we will explore different research directions. First of all we want to evaluate the tool on Italian data, even if we have not found a suitable gold standard so far. Resorting to crowd-sourcing may be a viable solution. We expect lower performances than the ones obtained for English, given that the current mapping between Open Multilingual WordNet and WordNet 3.0 covers only the 32.5\% of the English synsets: this consequently affects the mapping on the domains of WND. Moreover, the coverage of Italian in ConcepNet 5 is limited. %We also plan to extend the tool by taking into account also adjectives. As for the availability of L-KD, we plan to release the tool as a stand-alone module. It will also be integrated in the ALCIDE platform AUTHOR that supports the analysis of large document collections for humanities studies. The research leading to this paper was partially supported by the EU Horizon 2020 Programme via the SIMPATICO Project (H2020-EURO-6-2015, n. 692819). We thanks Alessio Palmero Aprosio for his help in the evaluation process.