Temat: natural language parsing - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: An Application of Probabilistic Grammars to Efficient Machne Translation
Autorzy:: Skórzewski, Paweł
Powiązania:: https://bibliotekanauki.pl/articles/1037598.pdf
Data publikacji:: 2010-09-15
Wydawca:: Uniwersytet im. Adama Mickiewicza w Poznaniu
Tematy:: a* algorithm
machine translation
natural language parsing
pcfg
probabilistic grammars
Opis:: In this paper we present one of the algorithms used to parse probabilistic context-free grammars: the A* parsing algorithm, which is based on the A* graph search method. We show an example of application of the algorithm in an existing machine translation system. The existing CYK-based parser used in the Translatica system was modified by applying the A* parsing algorithm in order to examine the possibilities of improving its performance. This paper presents the results of applying the A* algorithm with different heuristic functions and their impact on the performance of the parser.
Źródło:: Investigationes Linguisticae; 2010, 21; 90-98
1426-188X
1733-1757
Pojawia się w:: Investigationes Linguisticae
Dostawca treści:: Biblioteka Nauki

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Tytuł:: Is the Artificial Intelligent? A Perspective on AI-based Natural Language Processors
Autorzy:: Błachnio, Wojciech
Powiązania:: https://bibliotekanauki.pl/articles/601211.pdf
Data publikacji:: 2019
Wydawca:: Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:: Artificial Intelligence, Natural Language Processors, Fluid Construction Grammar, parsing, cognition
Opis:: The issue of the relation between AI and human mind has been riddling the scientific world since ages. Being the mother lode of research, AI can be scrutinised from a plethora of perspectives. One of them is a linguistic perspective, which encompasses AI’s capability to understand language. Having been an innate and exclusive faculty of human mind, language is now manifested in a countless number of ways, transcending beyond the human-only production. There are applications that can not only understand what is meant by an utterance, but also engage in a quasi-humane discourse. The manner of their operating is perfectly organised and can be accounted for by incorporating linguistic theories. The main theory used in this article is Fluid Construction Grammar, which has been developed by Luc Steels. It is concerned with parsing and segmentation of any utterance – two processes that are pivotal in AI’s understanding and production of language. This theory, in addition with five main facets of languages (phonological, morphological, semantic, syntactic and pragmatic) provides a valuable insight into the discrepancies between natural and artificial perception of language. Though there are similarities between them, the article shall conclude with what makes two adjacent capabilities different. The aim of this paper is to display the mechanisms of AI natural language processors with the aid of contemporary linguistic theories, and present possible issues which may ensue from using artificial language-recognising systems.
Źródło:: New Horizons in English Studies; 2019, 4
2543-8980
Pojawia się w:: New Horizons in English Studies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Using particle swarm optimization to accurately identify syntactic phrases in free text
Autorzy:: Tambouratzis, G.
Powiązania:: https://bibliotekanauki.pl/articles/91802.pdf
Data publikacji:: 2018
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: parsing of natural language
machine translation
syntactically-derived phrasing
particle swarm optimization (PSO)
PSO
parameter optimization
Adaptive PSO
AdPSO
Opis:: The present article reviews the application of Particle Swarm Optimization (PSO) algorithms to optimize a phrasing model, which splits any text into linguistically-motivated phrases. In terms of its functionality, this phrasing model is equivalent to a shallow parser. The phrasing model combines attractive and repulsive forces between neighbouring words in a sentence to determine which segmentation points are required. The extrapolation of phrases in the specific application is aimed towards the automatic translation of unconstrained text from a source language to a target language via a phrase-based system, and thus the phrasing needs to be accurate and consistent to the training data. Experimental results indicate that PSO is effective in optimising the weights of the proposed parser system, using two different variants, namely sPSO and AdPSO. These variants result in statistically significant improvements over earlier phrasing results. An analysis of the experimental results leads to a proposed modification in the PSO algorithm, to prevent the swarm from stagnation, by improving the handling of the velocity component of particles. This modification results in more effective training sequences where the search for new solutions is extended in comparison to the basic PSO algorithm. As a consequence, further improvements are achieved in the accuracy of the phrasing module.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 1; 63-77
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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