Tint, the Swiss-Army Tool for Natural Language Processing in Italian

Abstract

In this we paper present the last version of Tint, an opensource, fast and extendable Natural Language Processing suite for Italian based on Stanford CoreNLP. The new release includes a set of text processing components for ne-grained linguistic analysis, from tokenization to relation extraction, including part-of-speech tagging, morphological analysis, lemmatization, multi-word expression recognition, dependency parsing, named-entity recognition, keyword extraction, and much more. Tint is written in Java freely distributed under the GPL license. Although some modules do not perform at a state-of-the-art level, Tint reaches very good accuracy in all modules, and can be easily used outof-the-box.

Keywords

• Natural Language Processing
• Arti cial Intelligence
• Text Analysis
• Readability

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In this paper, we present Tint, a suite of ready-to-use modules for Italian NLP. Tint is free to use, open source, and can be downloaded and used out-of-the-box (see Section 5). Compared to the previous versions [ 26, 27 ], the suite has been enriched with several modules for ne-grained linguistic analysis that were not available for Italian before (for example, constituency parsing, relation extraction, temporal expression extraxtion) Finally, some other modules have been trained with new data (named-entity recognition and dependency parsing).

Related work

Most of the linguistic pipelines freely available for download (such as Stanford CoreNLP1 and OpenNLP2) are language independent and, even if they are not Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

1 http://stanfordnlp.github.io/CoreNLP/

2 https://opennlp.apache.org/

available in Italian out-of-the-box, they could be trained in every existing language. A notable examples in this direction are UDpipe [ 32 ], SpaCy [ 18 ], and Stanza [ 29 ], trainable pipelines which perform most of the common NLP tasks and are available in almost all the languages included in the Universal Dependencies [ 20 ], Freeling [ 25 ], a C++ library providing language analysis functionalities for a variety of languages. There are also some other pipelines speci cally written for Italian, such as TextPro [ 14 ], and T2K [ 13 ], but none of them are released as open source (and TextPro is the only one that can be downloaded and used for free for research purposes). 3

Modules

The Tint software is built on top of Stanford CoreNLP, a framework that helps users to derive linguistic annotations for text. Di erently from some similar tools, such as UIMA [ 15 ] and GATE [ 9 ], CoreNLP requires only basic object-oriented programming skills to extend it. The centerpiece of CoreNLP is the pipeline, that takes in raw text, runs a series of NLP annotators on the text, and produces a nal set of annotations. CoreNLP supports six languages (Arabic, Chinese, English, French, German, and Spanish). In Tint, we use the CoreNLP paradigm and bring it to Italian.

Among the modules, some of them have been implemented from scratch and do not rely on the components available in Stanford CoreNLP (for example: tokenization, morphological analysis, and so on). Other tasks, such as POS tagging and dependency parsing, are performed using the existing modules in CoreNLP, trained on Italian dataset available to the community. Finally, additional modules include wrappers for existing tools written in Java or available through a web API, such as keyword extraction and entity linking.

We mark with an asterisk (*) the modules that have never been described in a previous work, and with a dagger (†) the ones that were retrained or signi cantly renovated w.r.t. the past articles. 3.1

Tokenizer and sentence splitter

This module provides text segmentation in tokens and sentences. At rst, the text is grossly tokenized. Then, in a second step, tokens that need to be put together are merged using two customizable lists of Italian non-breaking abbreviations (such as \dott." or \S.p.A.") and regular expressions (for e-mail addresses, web URIs, numbers, emoticons). This second phase uses [ 11 ] to speedup the process. 3.2

Truecaser (*)

The truecase module recognizes the \true" case of tokens (how it would be capitalized in well-edited text) when this information is lost, e.g., all upper case text.

It is included in the CoreNLP original package,3 and relies on a discriminative model using the CRF sequence tagger. The model shipped with Tint has been trained on an Italian corpus (1.3 billion words) that includes texts from di erent domains: legal, narrative, news, and so on [ 37 ]. 3.3

Part-of-speech tagger

The part-of-speech annotation is provided through the Maximum Entropy implementation [ 39 ] included in Stanford CoreNLP. The model is trained on the Universal Dependencies (UD) dataset for Italian [ 6 ]. 3.4

Token splitter (*)

The tokenizer (see Section 3.1) is able to split a text into words, but the basic unit for a good morphological annotation is the syntactic word. This means

3 https://stanfordnlp.github.io/CoreNLP/truecase.html

that we systematically want to split o clitics, as in \dammelo" (verb \da", plus pronouns \me", and \lo"), and undo contractions, as in \alla", that is \a" (preposition) plus \la" (determiner). We refer to such cases as multiword tokens because a single orthographic token corresponds to multiple (syntactic) words.

The CoreNLP pipeline performs such task immediately after segmentation. However, in Italian the tokenization alone is not enough to understand whether a particular token needs to be split. For instance, depending on the context, \delle" can be both a partitive article and a contraction of a preposition and a determiner. Similarly, \porci" can be a noun or a verb plus clitic. We then write a new module for the purpose, using the information provided by the POS module to discriminate the ambiguous cases.

To ensure compatibility with the previous versions of Tint, all the modules provided in Tint that operate after part-of-speech are con gured to work either with and without the splitter. Modules that need the training of a model (such as dependency/constituency parsing and named-entity recognition) are trained in both setups: the two models are included in the Tint distribution (one can activate the right one in the con guration le). 3.5

Morphological analyzer

The morphological analyzer module provides the full list of morphological features for each annotated token. The current version of the module has been trained using the Morph-it lexicon [ 41 ], but it is possible to extend or retrain it with other Italian datasets. To extend the coverage of the results, especially for the complex forms, such as \porta-ce-ne" or \bi-direzionale", the module decomposes the token into pre x-root-in x-su x and tries to recognise the root form. 3.6

Lemmatizer (†)

The module for the lemmatization is a rule-based system that works by combining the part-of-speech output (Section 3.3) and the results of the morphological analyzer (Section 3.5) so to disambiguate the morphological features using the grammatical annotation. In order to increase the accuracy of the results, the module tries to detect the genre of noun lemmas relying on the analysis of their processed articles. For instance, for the correct lemmatization of \il latte/the milk", the module uses the singular article \il" to identify the correct gender/number of the lemma \latte" and returns \latte/milk" (male, singular) instead of \latta/metal sheet" (female, which plural form is \latte").

In addition, we developed a morphological guesser that is activated whenever a form cannot be linked to any lemma through the morphological analyzer (Section 3.5). Starting from the series form/lemma/pos in the Italian UD datasets, we trained a statistical model using decision trees and probabilities given by frequencies of certain su xes in the UD. For instance, starting from the nonexistent word \insalatando" tagged as verb (probably meaning eating salad), the guesser starts from the end of the form and, letter by letter, explores the tree of possibilities until it reaches a result with a reasonable accuracy.

The guesser is active by default, but can be deactivated when needed. When active, the guessed lemmas are tagged as such, so that the researcher (or the tool calling Tint) can use this information. 3.7

Verbal tenses classi er

Part-of speech tagger and morphological analyzer released with Tint can identify and classify verbs at token level, but sometimes the modality, form and tense of a verb is the result of a sequence of tokens, as in compound tenses such as participio passato, or passive verb forms. For example, in Italian the word siamo, takes as a single token, is the simple present form of the verb essere; if we look at the surrounding words, we can have forms such as siamo andati (present perfect of verb andare, active) or siamo mangiati (simple present of verb mangiare, passive). For this reason, we include in Tint a tense module to provide a more complete annotation of multi-token verbal forms. The module supports also the analysis of discontinuous expressions, like for example ho sempre mangiato. 3.8

A

xes annotator This module provides a token-level annotation about word derivatives, based on derIvaTario [ 35 ],4 a resource manually created to achieve a high accuracy and overcome errors coming from resources developed in a semi-automatic way [ 42, 36 ]. The dataset was built segmenting into derivational cycles about 11,000 derivatives and annotating them with a wide array of features. The module uses this resource in input to segment a token into root and a xes, for example visione is analysed as baseLemma=vedere, a x=zione and allomorph=ione. 3.9

Multi-word expressions extractor

A speci c multi-token annotator has been implemented to recognize more than 13,450 multi-word expressions, the so-called `polirematiche' [ 40 ], manually collected from various online resources. The list includes verbal, nominal, adjectival and prepositional expressions (e.g. lasciar perdere, societa per azioni, nei confronti di, mezzo morto). This annotator can identify also discontinuous multiwords. For example, in the expression andare a genio (Italian phrase that means \to like") an adverb can be included, as in andare troppo a genio. Similarly, in such phrases one can nd nouns and adjectives (e.g. lasciare Antonio a piedi, where lasciare a piedi is an Italian multiword for leave stranded ).

4 http://derivatario.sns.it/

3.10

Named-entities recognizer (†)

The NER module recognize persons, locations and organizations in the text. It uses a CRF sequence tagger [ 16 ] included in Stanford CoreNLP and it is trained on KIND [ 23 ], a dataset containing around 340K words taken from Wikinews.

To enhance the classi cation, Stanford NER also accepts gazettes of names labelled with the corresponding tag. We collect a list of persons, organizations and locations from the Italian Wikipedia using some classes in DBpedia [ 3 ]: Person, Organisation, and Place, respectively. In addition to this, we collect the list of streets from OpenStreetMap [ 22 ], limiting the extraction to Italian names. 3.11

Temporal expressions extractor and normalizer (*)

Since the rst version of Tint, the task of temporal expression extraction was provided as a wrapper to HeidelTime [ 33 ], a rule-based state-of-the-art temporal tagger developed at Heidelberg University.

The original English version of CoreNLP uses SUTime [ 7 ], a powerful library for processing temporal expressions, built on top of TokensRegex, a framework for de ning regular expressions over text and tokens, and mapping matched text to semantic objects. The current version of Tint uses SUTime and a new set of rules written for Italian. It also normalizes the expressions according to the TIMEX3 annotation standard. SUTime is generally run as a subcomponent of the named-entities recognizer annotator (Section 3.10) and is active by default (it can be disabled if not needed).

Recognized temporal expressions can be resolved relative to the document date. For instance, the expression \mercoled scorso" will be resolved to the Wednesday that is immediately before to the document date, be it the current date or any other date. The document date can be set when Tint is launched, otherwise current date and time are used. 3.12

Constituency parser (*)

A constituency parser is a program that works out the grammatical structure of sentences, for instance, which groups of words go together (as \phrases" and which words are the subject or object of a verb. In Tint this task is performed by shift-reduce [ 43 ] parser module included in Stanford CoreNLP.

Data used for training is taken from both the Turin University Treebank [ 5 ] and the Parallel TUT [ 30 ]. Their licence allows to use it for research purposes.

These treebanks cannot be used as is, because the multiword tokens (see Section 3.4) are denoted by doubling the token. In addition, part-of-speech tags and some constituency labels need to be replaces to make the dataset compatible with the CoreNLP parser. Conversion rules for both tagsets are included in the Tint release. A script to apply the conversion to the dataset is also included. 3.13

Dependency parser (†)

This module provides syntactic analysis of the text and uses a transition-based parser (included in Stanford CoreNLP) which produces typed dependency parses of natural language sentences [ 8 ]. The parser is powered by a neural network which accepts word embedding inputs: the model is trained on the UD dataset and the word embeddings are built on the corpus described in Section 3.2. 3.14

Relation extraction

New regulations on transparency and the recent policy for privacy force the public administration (PA) to make their documents available, but also to limit the di usion of personal data. The relation extraction module represents a rst approach to the extraction of sensitive data from PA documents in terms of named entities and semantic relations among them.

For this task, we rely on the Relation Extraction module [ 34 ] included in Stanford CoreNLP. For this module to work, a relation must connect two entities. For instance, address is used for instance to link a LOC entity representing an address to the person or company to which the address belongs, while birthDate, birthLoc link respectively the date and location of birth.

Some entities are extracted using the named-entities recognizer (Section 3.10) and the temporal expression extractor (Section 3.11). To deal with all the requested relations, some additional entity types are manually added and annotate using the TokenRegexp CoreNLP module (see Section 3.11). Additional entities include, for example, NUMBER for numbers (such as VAT), CF for the Italian \codice scale" sequence of chars, ROLE for personal and organisation roles, and so on.

To train the relation extraction module, we use the REDIT dataset, containing documents taken from the PA domain and manually annotated with 19 relations [ 24 ]. 3.15

Text reuse

Detecting text reuse is useful when, in a document, we want to measure the overlap with a given corpus. This is needed in a number of applications, for example for plagiarism detection, stylometry, authorship attribution, citation analysis, etc. Tint includes a component to deal with this task, i.e. identifying parts of an input text that overlap with a given corpus. First of all, each sentence of the corpus is compared with the sentences in the processed text using the FuzzyWuzzy package5, a Java fuzzy string matching implementation: this allows the system not to miss expressions that are slightly di erent with respect to the texts in the original corpus. In this phase, only long spans of text can be considered, as the probability of an incorrect match on fuzzy comparison grows as soon as the text length decreases. A second step checks whether the overlap involves the whole sentence and, if not, it analyzes the two texts and identi es the number of overlapping tokens. Finally, the Stanford CoreNLP quote annotator6 is used to catch text reuse that is in between quotes, ignoring the length limitation of the fuzzy comparison. 3.16

Readability and corpus statistics

In this module, we compute some metrics that can be useful to assess the readability of a text, partially inspired by [ 12 ] and [ 38 ]. In particular, we include the following indices: { Number of content words, hyphens (using iText Java Library7), sentences having less than a xed number of words, distribution of tokens based on part-of-speech. { Type-token ratio (TTR), i.e. the ratio between the number of di erent lemmas and the number of tokens; high TTR indicates a high degree of lexical variation. { Lexical density, i.e. the number of content words divided by the total number of words.

5 https://github.com/xdrop/fuzzywuzzy

6 https://stanfordnlp.github.io/CoreNLP/quote.html

7 https://github.com/itext/itextpdf

{ Amount of coordinate and subordinate clauses, along with the ratio between them. { Depth of the parse tree for each sentence: both average and max depth are calculated on the whole text. { Gulpease formula [ 19 ] to measure the readability at document level. { Text di culty based on word lists from De Mauro's Dictionary of Basic Italian8.

In addition, a set of extractors described in [ 31 ] and mainly de ning the neostandard Italian used by high school students (such as anglicisms, euphonic \D", and much more) are available out-of-the-box in Tint.

Finally, a collection of CoreNLP annotators have been developed to extract statistics that can be used, for instance, to analyse traits of interest in texts. More speci cally, the provided modules can mark and compute words and sentences based on token, lemma, part-of-speech and word position in the sentence. 3.17

Keywords extraction

Keyword extraction in Tint is performed by Keyphrase Digger9 [ 21 ], a rule-based system for keyphrase extraction. It combines statistical measures with linguistic information given by part-of-speech patterns to identify and extract weighted keyphrases from texts. 3.18

Entity linking

The entity linking task consists in disambiguating a word (or a set of words) and link them to a knowledge base (KB). The biggest (and most used) available KB is Wikipedia, and almost every linking tool relies on it. The Tint pipeline provides a wrapper annotator that can connect to DBpedia Spotlight10 [ 10 ] and The Wiki Machine11 [ 2 ]. Both tools are distributed as open source software and can be used by the annotator both as external services or through a local installation. 4

Evaluation

Tint is a complex system that relies on a variety of modules interacting each other. For this reason, the accuracy on the single tasks does not reach state-ofthe-art accuracy. Nevertheless, Tint performs as a reasonable level in all tasks it performs.

An accurate evaluation of most of its modules (especially the ones that use machine learning techniques), with a comparison with other NLP Italian tools, is available in the previous papers [ 26, 27, 24 ].

8 http://bit.ly/nuovo-demauro

9 https://dh.fbk.eu/2015/12/kd-keyphrase-digger/

10 https://www.dbpedia-spotlight.org/ 11 https://bitbucket.org/fbk/airpedia/wiki/Tutorial

Tint distibution

The Tint pipeline is released as an open source software under the GNU General Public License (GPL), version 3. It can be download from the Tint website12 as a standalone package, or it can be integrated into an existing application as a Maven dependency. The source code is available on Github.13

The tool is written using the Stanford CoreNLP paradigm, therefore a third part software can be integrated easily into the pipeline.

Along with Tint, one can also try Tintful14 [ 17 ], a NLP annotation software that can be used both to manually annotate texts and to x mistakes in NLP pipelines (and, in particular, in Tint). Using a paradigm similar to wiki-like systems, a user who notices some wrong annotation can easily x it and submit the resulting (and right) entry back to the tool developers. The Tint online demo, linked from the project website, uses Tintful as graphical interface and is con gured to show most of the modules described in this paper. Therefore the annotation provided by the modules working on machine learning algorithms that need to be trained over annotated data (named-entity recognizer, part-ofspeech tagger, dependency parser) can be edited by the occasional user. The resulting annotation will be manually checked by linguists and added to the next training session. Figure 3 shows the web interface of Tintful. 12 http://tint.fbk.eu/ 13 https://github.com/dhfbk/tint/ 14 https://github.com/dhfbk/tintful

Conclusions and Future Work

In this paper, we presented the last release of Tint, a simple, fast and accurate NLP pipeline for Italian, based on Stanford CoreNLP. In the new version, we xed some bugs and improved some of the existing modules. We also added a set of components for ne-grained linguistics analysis that were not available so far.

In the future, we plan to improve the suite and extend it with additional modules, in particular Word Sense Disambiguation (WSD) based on linguistic resources such as MultiWordNet [ 28 ] and Semantic Role Labelling, by porting to Italian resources such as FrameNet [ 4 ], now available only in English.

We also plan to increase the accuracy of the trained modules (such as partof-speech tagger and named-entity recognizer) using deep learning techniques and including a pretrained language model at a di erent granularity (words, characters) into the process [ 1 ].

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