scholarly journals French de and en as expressions of the genitive case: a unified analysis within LFG and computational implementation in XLE

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Leonel Figueiredo de Alencar ◽  
Christoph Schwarze
Keyword(s):  
Natural Language ◽  
Deep Structure ◽  
Functional Grammar ◽  
Lexical Functional Grammar ◽  
Heterogeneous Structures ◽  
Wide Range ◽  
Computational Implementation ◽  
Systematic Relationships ◽  
Transformational Framework ◽  
Genitive Case

ABSTRACT The French clitic pro-form en represents a wide range of heterogeneous constituents: de-PP complements and adjuncts, partitive objects, and prepositionless objects of cardinals. The main goal of this paper is to formalize this relationship computationally in terms of genitive case. This is apparently the first non-transformational counterpart to Kayne (1975)’s unified analysis, which derives en from a deep structure with de by means of syntactic transformations. Transformational grammars are problematic from the parsing perspective. In order to test our analysis automatically on a large amount of data, we implemented it in a computational grammar of French in the Lexical-Functional Grammar (LFG) formalism using the XLE system. This non-transformational framework is particularly fit for expressing systematic relationships between heterogeneous structures and has successfully been used for the implementation of natural language grammars since the 1980s. We tested the implementation on 320 grammatical sentences and on an equal number of ungrammatical examples. It analyzed all grammatical examples and blocked almost 95% of the ungrammatical ones, showing a high empirical adequacy of the grammar.

scholarly journals Revisiting the configurationality issue in Old Icelandic

2021 ◽  
Author(s):  
Hannah Booth
Keyword(s):  
Word Order ◽  
Early Stage ◽  
Functional Grammar ◽  
Old Icelandic ◽  
Modern Language ◽  
Lexical Functional Grammar ◽  
Wide Range ◽  
The Status ◽  
Corpus Data

The status of Old Icelandic with respect to (argument) configurationality was subject to debate in the early 1990s (e.g. Faarlund 1990; Rögnvaldsson 1995) and remains unresolved. Since this work, further research on a wide range of languages has enhanced our understanding of configurationality, in particular within Lexical Functional Grammar (e.g. Austin & Bresnan 1996; Nordlinger 1998) and syntactically annotated Old Icelandic data are now available (Wallenberg et al. 2011). It is thus fitting to revisit the matter. In this paper, I show that allowing for argument configurationality as a gradient property, and also taking into account discourse configurationality (Kiss 1995) as a further gradient property, can neatly account for word order patterns in this early stage of Icelandic. Specifically, I show that corpus data supports part of the original claim in Faarlund (1990), that Old Icelandic lacks a VP-constituent, thus being somewhat less argument-configurational than the modern language. Furthermore, the observed word order patterns indicate a designated topic position in the postfinite domain, thus reflecting some degree of discourse configurationality at this early stage of the language.

scholarly journals Tractable Lexical-Functional Grammar

2020 ◽  
Vol 46 (3) ◽  
pp. 515-569
Author(s):  
Jürgen Wedekind ◽  
Ronald M. Kaplan
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Expressive Power ◽  
Functional Grammar ◽  
Natural Languages ◽  
Worst Case ◽  
Lexical Functional Grammar ◽  
Rewriting Systems ◽  
Mathematical Properties ◽  
Context Free

The formalism for Lexical-Functional Grammar (LFG) was introduced in the 1980s as one of the first constraint-based grammatical formalisms for natural language. It has led to substantial contributions to the linguistic literature and to the construction of large-scale descriptions of particular languages. Investigations of its mathematical properties have shown that, without further restrictions, the recognition, emptiness, and generation problems are undecidable, and that they are intractable in the worst case even with commonly applied restrictions. However, grammars of real languages appear not to invoke the full expressive power of the formalism, as indicated by the fact that algorithms and implementations for recognition and generation have been developed that run—even for broad-coverage grammars—in typically polynomial time. This article formalizes some restrictions on the notation and its interpretation that are compatible with conventions and principles that have been implicit or informally stated in linguistic theory. We show that LFG grammars that respect these restrictions, while still suitable for the description of natural languages, are equivalent to linear context-free rewriting systems and allow for tractable computation.

Typological and theoretical implications

2017 ◽  
Author(s):  
John J. Lowe
Keyword(s):  
Theoretical Analysis ◽  
Theoretical Perspective ◽  
Functional Grammar ◽  
Lexical Functional Grammar ◽  
Mixed Categories

This chapter briefly considers the evidence for transitive nouns and adjectives in early Indo-Aryan in both a typological and a theoretical perspective. The fact that most transitive nouns and adjectives in early Indo-Aryan fall under the traditional heading of ‘agent nouns’ (subject-oriented formations) is typologically notable, since while action nouns with verbal government are well-known, the possibility of relatively verbal agent nouns has not always been acknowledged. The theoretical analysis is framed within Lexical-Functional Grammar, and makes use of the concept of ‘mixed’ categories to effect a clear formalization of transitive nouns and adjectives which captures their transitivity while allowing them to remain fundamentally nouns and adjectives in categorial terms.

Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language

2021 ◽  
Vol 30 (1) ◽  
pp. 774-792
Author(s):  
Mazin Abed Mohammed ◽  
Dheyaa Ahmed Ibrahim ◽  
Akbal Omran Salman
Keyword(s):  
Natural Language ◽  
Naive Bayes ◽  
False Negative ◽  
Naïve Bayes ◽  
Final Decision ◽  
Learning Approach ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Wide Range

Abstract Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this approach is for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest.

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