Search

This paper describes the first edition of the “Diachronic Lexical Seman-tics” (DIACR-Ita) task at the EVALITA2020 campaign. The task challenges participants to develop systems that can automatically detect if a given word has changed its meaning over time, given con-textual information from corpora.The task, at its first edition, attracted 9 participant teams and collected a total of 36 sub-mission runs

In this paper, we describe the creation of a diachronic corpus for Italian by exploiting the digital archive of the newspaper “L’Unit`a”. We automatically clean and annotate the corpus with PoStags, lemmas, named entities and syntactic dependencies. Moreover, we compute frequency-based time series for tokens, lemmas and entities. We show some interesting corpus statistics taking into account the temporal dimension and describe some examples of usage of time series.

This paper describes the system proposed by the Random team for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. We focus our approach on the detection problem. Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or lost senses. To this end, we define a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods. We compare the proposed approach with a number of similarity-based thresholds. We found that, although the performance of the detection methods varies across the word embedding algorithms, the combination of Gaussian Mixture with Temporal Referencing resulted in our best system.

During the last decade the surge in available data spanning different epochs has inspired a new analysis of cultural, social, and linguistic phenomena from a temporal perspective. This paper describes a method that enables the analysis of the time evolution of the meaning of a word. We propose Temporal Random Indexing (TRI), a method for building WordSpaces that takes into account temporal information. We exploit this methodology in order to build geometrical spaces of word meanings that consider several periods of time. The TRI framework provides all the necessary tools to build WordSpaces over different time periods and perform such temporal linguistic analysis. We propose some examples of usage of our tool by analysing word meanings in two corpora: a collection of Italian books and English scientific papers about computational linguistics. This analysis enables the detection of linguistic events that emerge in specific time intervals and that can be related to social or cultural phenomena.

Named Entity Linking (NEL) is the task of semantically annotating entity mentions in a portion of text with links to a knowledge base. The automatic annotation, which requires the recognition and disambiguation of the entity mention, usually exploits contextual clues like the context of usage and the coherence with respect to other entities. In Twitter, the limits of 140 characters originates very short and noisy text messages that pose new challenges to the entity linking task. We propose an overview of NEL methods focusing on approaches specifically developed to deal with short messages, like tweets. NEL is a fundamental task for the extraction and annotation of concepts in tweets, which is necessary for making the Twitter’s huge amount of interconnected user-generated contents machine readable and enable the intelligent information access.

Polysemous words have more than one possible meaning, thus word ambiguity is a key issue for the systems which access textual information. Computational linguistics proposes two main methods to cope with word ambiguity: sense disambiguation and sense discrimination. (Word) Sense Disambiguation is the task of selecting a sense for a word from a set of predefined possibilities, while (Word) Sense Discrimination is the task of dividing the usages of a word into different meanings, discriminating among word meanings based on information found in unannotated corpora. This paper proposes a strategy to compare disambiguation and discrimination systems by adopting an "in vivo" evaluation in an Information Retrieval scenario. The goal of the evaluation is to establish how disambiguation and discrimination bias the retrieval performance.

In this paper, we propose several methods for the diachronic analysis of the Italian language. We build several models by exploiting Temporal Random Indexing and the Google Ngram dataset for the Italian language. Each proposed method is evaluated on the ability to automatically identify meaning shift over time. To this end, we introduce a new dataset built by looking at the etymological information reported in some dictionaries. In questo lavoro proponiamo alcuni metodi per l’analisi diacronica della lingua italiana. Abbiamo costruito differenti modelli utilizzando la tecnica del Temporal Random Indexing e Google Ngram per l’italiano. Ciascun metodo proposto è stato valutato rispetto alla capacità di identificare automaticamente i cambi di significato nel tempo. A tale scopo introduciamo uno nuovo dataset costruito mediante le informazioni etimologiche presenti in alcuni dizionari.

This report describes the main outcomes of the 2016 Named Entity rEcognition and Linking in Italian Tweet (NEEL-IT) Challenge. The goal of the challenge is to provide a benchmark corpus for the evaluation of entity recognition and linking algorithms specifically designed for noisy and short texts, like tweets, written in Italian. The task requires the correct identification of entity mentions in a text and their linking to the proper named entities in a knowledge base. To this aim, we choose to use the canonicalized dataset of DBpedia 2015-10. The task has attracted five participants, for a total of 15 runs submitted. In questo report descriviamo i principali risultati conseguiti nel primo task per la lingua Italiana di Named Entity rEcognition e Linking in Tweet (NEEL-IT). Il task si prefigge l’obiettivo di offrire un framework di valutazione per gli algoritmi di riconoscimento e linking di entità a nome proprio specificamente disegnati per la lingua italiana per testi corti e rumorosi, quali i tweet. Il task si compone di una fase di riconoscimento delle menzioni di entità con nome proprio nel testo e del loro successivo collegamento alle opportune entità in una base di conoscenza. In questo task abbiamo scelto come base di conoscenza la versione canonica di DBpedia 2015. Il task ha attirato cinque partecipanti per un totale di 15 diversi run.

This paper describes the first edition of the Question Answering for Frequently Asked Questions (QA4FAQ) task at the EVALITA 2016 campaign. The task concerns the retrieval of relevant frequently asked questions, given a user query. The main objective of the task is the evaluation of both question answering and information retrieval systems in this particular setting in which the document collection is composed of FAQs. The data used for the task are collected in a real scenario by AQP Risponde, a semantic retrieval engine used by Acquedotto Pugliese (AQP, the Organization for the management of the public water in the South of Italy) for supporting their customer care. The system is developed by QuestionCube, an Italian startup company which designs Question Answering tools. Questo lavoro descrive la prima edizione del Question Answering for Frequently Asked Questions (QA4FAQ) task proposto durante la campagna di valutazione EVALITA 2016. Il task consiste nel recuperare le domande più frequenti rilevanti rispetto ad una domanda posta dallʼutente. Lʼobiettivo principale del task è la valutazione di sistemi di question answering e di recupero dellʼinformazione in un contesto applicativo reale, utilizzando i dati provenienti da AQP Risponde, un motore di ricerca semantico usato da Acquedotto Pugliese (AQP, lʼente per la gestione dellʼacqua pubblica nel Sud Italia). Il sistema è sviluppato da QuestionCube, una startup italiana che progetta soluzioni di Question Answering.