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1	A Refutation of Finite-State Language Models through Zipf’s Law for Factual Knowledge
	Łukasz Dębowski
	In: Entropy ; Volume 23 ; Issue 9 (2021)
	Abstract: We present a hypothetical argument against finite-state processes in statistical language modeling that is based on semantics rather than syntax. In this theoretical model, we suppose that the semantic properties of texts in a natural language could be approximately captured by a recently introduced concept of a perigraphic process. Perigraphic processes are a class of stochastic processes that satisfy a Zipf-law accumulation of a subset of factual knowledge, which is time-independent, compressed, and effectively inferrable from the process. We show that the classes of finite-state processes and of perigraphic processes are disjoint, and we present a new simple example of perigraphic processes over a finite alphabet called Oracle processes. The disjointness result makes use of the Hilberg condition, i.e., the almost sure power-law growth of algorithmic mutual information. Using a strongly consistent estimator of the number of hidden states, we show that finite-state processes do not satisfy the Hilberg condition whereas Oracle processes satisfy the Hilberg condition via the data-processing inequality. We discuss the relevance of these mathematical results for theoretical and computational linguistics.
	Keyword: algorithmic mutual information; hidden Markov processes; Hilberg’s hypothesis; perigraphic processes; statistical language modeling; Zipf’s law
	URL: https://doi.org/10.3390/e23091148
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2	STATISTICAL RELATIONAL LEARNING AND SCRIPT INDUCTION FOR TEXTUAL INFERENCE
	Mooney,Raymond. - 2017
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3	Confound and control in language experiments ...
	Alday, Phillip M.; Sassenhagen, Jona. - : figshare, 2016
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4	Confound and control in language experiments ...
	Alday, Phillip M.; Sassenhagen, Jona. - : figshare, 2016
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5	Structural Complexity in Linguistic Systems Research Topic 3: Mathematical Sciences
	Crutchfield, James P
	In: DTIC (2015)
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6	A Fast Variational Approach for Learning Markov Random Field Language Models
	Jernite, Yacine; Rush, Alexander M; Sontag, David
	In: DTIC (2015)
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7	Learning to Understand Natural Language with Less Human Effort
	Krishnamurthy, Jayant
	In: DTIC (2015)
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8	The role of markup in the digital humanities
	Schmidt, Desmond
	In: Historical Social Research ; 37 ; 3 ; 125-146 ; Kontroversen um die Digitalen Geisteswissenschaften / Controversies around the digital humanities (2015)
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9	Mandarin listeners can learn non-native lexical tones through distributional learning
	Ong, Jia (S31400); Burnham, Denis K. (R7357); Escudero, Paola (R16636). - : U.K., University of Glasgow, 2015
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10	A nonparametric Bayesian perspective for machine learning in partially-observed settings ...
	Akova, Ferit. - : IUPUI University Library, 2014
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11	A nonparametric Bayesian perspective for machine learning in partially-observed settings
	Akova, Ferit. - 2014
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12	The Negations of Conjunctions, Conditionals, and Disjunctions
	Khemlani, Sangeet; Orenes, Isabel; Johnson-Laird, P N
	In: DTIC (2014)
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13	What's Wrong With Automatic Speech Recognition (ASR) and How Can We Fix It?
	Morgan, Nelson; Wegmann, Steven; Cohen, Jordan
	In: DTIC (2013)
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14	Identität in Erzählung und im Erzählen: Versuch einer Bestimmung der Besonderheit des narrativen Diskurses für die sprachliche Verfassung von ldentität
	Bamberg, Michael
	In: Journal für Psychologie ; 7 ; 1 ; 43-55 (2012)
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15	Coherent Demodulation of Nonstationary Random Processes
	Clark, Charles Pascal. - 2012
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16	Incremental Syntactic Language Models for Phrase-Based Translation
	Schwartz, Lane; Callison-Burch, Chris; Schuler, William...
	In: DTIC (2011)
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17	Using Linguistic Knowledge in Statistical Machine Translation
	Zbib, Rabih M.
	In: DTIC (2010)
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18	Related Entity Finding: University of Waterloo at TREC 2010 Entity Track
	Vechtomova, Olga
	In: DTIC (2010)
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19	Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques
	Ge, Ruifang
	In: DTIC (2010)
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20	Gibbs Sampling for the Uninitiated
	Resnik, Philip; Hardisty, Eric
	In: DTIC (2010)
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