Abstract
This study will present a tool designed for meaning extraction with monoclausal sentences in Italian. Its main features will be illustrated with instances of the Italian causative clause type featuring the verb fare ‘make’ (e.g. Egli fa piangere il bambino ‘He makes the child cry’), a construction which invariably embeds a clause (e.g. Il bambino piange ‘The child cries’) with which it establishes an entailment relationship. The tool automatically accomplishes the following tasks: it answers relevant wh- questions (e.g. Who cries? Who makes someone cry?) and detects the entailment. Concurrent with this presentation, this study will also encourage a reflection on the research currently being conducted in Computational Linguistics and Natural Language Processing, in which data-driven approaches prevail over rule-based systems.
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Notes
- 1.
The tool label includes: (a) the acronym NLP, (b) the initial letters of the programming language deployed, that is Python (3.8), (c) the Italian TAL acronym for NLP (Trattamento Automatico della Lingua), and (d) a revised spelling of the language being analysed. The system does not currently access Python libraries for use with NLP, excluding NLTK, employed for text tokenisation. NLPYTALY makes use of TreeTagger, a probabilistic tagger (the parameter files are those by Helmut Schmid, https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/). The tagset is by Achim Stein (https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/data/italian-tagset.txt). A repository of the tool, which currently includes the code used for the parsing and the detection of e.g. diathesis and constituency, is available on GitHub at the following link: ignaziomirto2017/nlpytaly.
- 2.
When the infinitive of a fare-causative is an intransitive verb with no transitive counterpart (NLPYTALY contains about 1,000 of such verbs), its subject almost always surfaces as a direct object, and the correct answers (e.g. (1c)) can be easily calculated. With the subject of transitive verbs, however, a few idiosyncratic pairings (i.e. misalignments) may occur, e.g. with aspettare ‘to wait’. Such verbs require an ad hoc treatment.
- 3.
This choice depends on the [± Animate] value characterizing nouns and noun phrases.
- 4.
See https://framenet.icsi.berkeley.edu/fndrupal/ (last consulted 12/12/2020).
- 5.
- 6.
Another semantic model based on predicate-argument relations is known as Tectogrammatical representation. See https://ufal.mff.cuni.cz/pedt1.0/tecto.html and [18].
- 7.
A promising model of vocabulary reduction could be that of dictionaries destined for use by non-native learners, in which the number of headwords (paralleled with columns A and B) far exceeds the number of words used in the definitions (paralleled with the metalanguage in column C).
- 8.
The Call For Papers is available at the following address: https://www.aclweb.org/portal/content/second-international-workshop-designing-meaning-representations-revised-call-papers .
- 9.
With regard to the performance of models based on semantic similarity, Stephen Clark ([19: 522]) states the following: “whether fundamental concepts of semantics, such as inference, can be suitably incorporated into vector space models of meaning is an open question”.
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Mirto, I.M. (2022). Measuring Meaning. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_70
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