On Hilberg's Law and Its Links with Guiraud's Law

Hilberg (1990) supposed that finite-order excess entropy of a random human text is proportional to the square root of the text length. Assuming that Hilberg's hypothesis is true, we derive Guiraud's law, which states that the number of word types in a text is greater than proportional to the square root of the text length. Our derivation is based on some mathematical conjecture in coding theory and on several experiments suggesting that words can be defined approximately as the nonterminals of the shortest context-free grammar for the text. Such operational definition of words can be applied even to texts deprived of spaces, which do not allow for Mandelbrot's ``intermittent silence'' explanation of Zipf's and Guiraud's laws. In contrast to Mandelbrot's, our model assumes some probabilistic long-memory effects in human narration and might be capable of explaining Menzerath's law.Comment: To appear in Journal of Quantitative Linguistic

Electronic Dictionaries and Transducers for Automatic Processing of the Albanian Language

International audienceWe intend on developing electronic dictionaries and Finite State Transducers for the automatic processing of the Albanian Language. We describe some peculiarities of this language and we explain how FST and generally speaking NooJ's graphs enable to treat them. We point out agglutinated words, mixed words or ‘XY' words that are not (or cannot be) listed into dictionaries and we use FST for their dynamic treatment. We take into consideration the problem of unknown words in a lately reformed language and the evolving of features in the dictionaries

Finding structure in language

Since the Chomskian revolution, it has become apparent that natural language is richly structured, being naturally represented hierarchically, and requiring complex context sensitive rules to define regularities over these representations. It is widely assumed that the richness of the posited structure has strong nativist implications for mechanisms which might learn natural language, since it seemed unlikely that such structures could be derived directly from the observation of linguistic data (Chomsky 1965).This thesis investigates the hypothesis that simple statistics of a large, noisy, unlabelled corpus of natural language can be exploited to discover some of the structure which exists in natural language automatically. The strategy is to initially assume no knowledge of the structures present in natural language, save that they might be found by analysing statistical regularities which pertain between a word and the words which typically surround it in the corpus.To achieve this, various statistical methods are applied to define similarity between statistical distributions, and to infer a structure for a domain given knowledge of the similarities which pertain within it. Using these tools, it is shown that it is possible to form a hierarchical classification of many domains, including words in natural language. When this is done, it is shown that all the major syntactic categories can be obtained, and the classification is both relatively complete, and very much in accord with a standard linguistic conception of how words are classified in natural language.Once this has been done, the categorisation derived is used as the basis of a similar classification of short sequences of words. If these are analysed in a similar way, then several syntactic categories can be derived. These include simple noun phrases, various tensed forms of verbs, and simple prepositional phrases. Once this has been done, the same technique can be applied one level higher, and at this level simple sentences and verb phrases, as well as more complicated noun phrases and prepositional phrases, are shown to be derivable

16th International NooJ 2022 Conference: Book of Abstracts

Libro de resúmenes presentados en la "16th International NooJ 2022 Conference", de modalidad híbrida, realizada en el ECU (Espacio Cultural Universitario, UNR) en Rosario, Santa Fe, Argentina, entre el 14 y 15 de junio de 2022.Fil: Reyes, Silvia Susana. Universidad Nacional de Rosario. Facultad de Humanidades y Artes; Argentin

Entry Generation for New Words by Analogy for Morphological Lexicons

Peer reviewe

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems