scholarly journals Automatic identification of semantic relations in Italian complex nominals

2009 ◽  
Author(s):  
Fabio Celli ◽  
Malvina Nissim
Keyword(s):  
Semantic Relations ◽  
Automatic Identification ◽  
Complex Nominals

scholarly journals General and specific paraphrases of semantic relations between nouns

2013 ◽  
Vol 19 (3) ◽  
pp. 357-384 ◽  
Author(s):  
PAUL NULTY ◽  
FINTAN COSTELLO
Keyword(s):  
New York ◽  
Probabilistic Method ◽  
Semantic Relations ◽  
Semantic Interpretation ◽  
Academic Press ◽  
Class A ◽  
Modern Sculpture ◽  
Complex Nominals ◽  
Relation Classification ◽  
Computational Systems

AbstractMany English noun pairs suggest an almost limitless array of semantic interpretation. A fruit bowl might be described as a bowl for fruit, a bowl that contains fruit, a bowl for holding fruit, or even (perhaps in a modern sculpture class), a bowl made out of fruit. These interpretations vary in syntax, semantic denotation, plausibility, and level of semantic detail. For example, a headache pill is usually a pill for preventing headaches, but might, perhaps in the context of a list of side effects, be a pill that can cause headaches (Levi, J. N. 1978. The Syntax and Semantics of Complex Nominals. New York: Academic Press.). In addition to lexical ambiguity, both relational ambiguity and relational vagueness make automatic semantic interpretation of these combinations difficult. While humans parse these possibilities with ease, computational systems are only recently gaining the ability to deal with the complexity of lexical expressions of semantic relations. In this paper, we describe techniques for paraphrasing the semantic relations that can hold between nouns in a noun compound, using a semi-supervised probabilistic method to rank candidate paraphrases of semantic relations, and describing a new method for selecting plausible relational paraphrases at arbitrary levels of semantic specification. These methods are motivated by the observation that existing semantic relation classification schemes often exhibit a highly skewed class distribution, and that lexical paraphrases of semantic relations vary widely in semantic precision.

Multi-word term variation

2021 ◽  
Vol 34 (2) ◽  
pp. 402-434
Author(s):  
Melania Cabezas-García ◽  
Santiago Chambó
Keyword(s):  
Language Processing ◽  
Semantic Relations ◽  
Conceptual Combination ◽  
Term Variation ◽  
Discourse Studies ◽  
Specialized Texts ◽  
Complex Nominals ◽  
Germanic Languages ◽  
Language Corpus ◽  
Productive Method

Abstract Complex nominals (CNs) are frequently found in specialized discourse in all languages, since they are a productive method of creating terms by combining existing lexical units. In Spanish, a conceptual combination may often be rendered with a prepositional CN (PCN) or an equivalent adjectival CN (ACN), e.g., demanda de electricidad vs. demanda eléctrica [electricity demand]. Adjectives in ACNs – usually derived from nouns – are known as ‘relational adjectives’ because they encode semantic relations with other concepts. With recent exceptions, research has focused on the underlying semantic relations in CNs. In natural language processing, several works have dealt with the automatic detection of relation adjectives in Romance and Germanic languages. However, there is no discourse studies of these CNs, to our knowledge, for the goal of establishing writer recommendations. This study analyzed the co-text of equivalent PCNs and ACNs to identify factors governing the use of a certain form. EcoLexicon ES, a corpus of Spanish environmental specialized texts, was used to extract 6 relational adjectives and, subsequently, a set of 12 pairs of equivalent CNs. Their behavior in co-text was analyzed by querying EcoLexicon ES and a general language corpus with 20 expressions in CQP-syntax. Our results showed that immediate linguistic co-text determined the preference for a particular structure. Based on these findings, we provide writing guidelines to assist in the production of CNs.

Accuracy of Four Language Analysis Procedures Performed Automatically

2001 ◽  
Vol 10 (2) ◽  
pp. 180-188 ◽  
Author(s):  
Steven H. Long ◽  
Ron W. Channell
Keyword(s):  
Automatic Identification ◽  
Published Data ◽  
Typically Developing ◽  
Language Impaired ◽  
Productivity Data ◽  
Language Analysis ◽  
Word Classes ◽  
Human Coder ◽  
Impaired Children ◽  
Metalinguistic Skills

Most software for language analysis has relied on an interaction between the metalinguistic skills of a human coder and the calculating ability of the machine to produce reliable results. However, probabilistic parsing algorithms are now capable of highly accurate and completely automatic identification of grammatical word classes. The program Computerized Profiling combines a probabilistic parser with modules customized to produce four clinical grammatical analyses: MLU, LARSP, IPSyn, and DSS. The accuracy of these analyses was assessed on 69 language samples from typically developing, speech-impaired, and language-impaired children, 2 years 6 months to 7 years 10 months. Values obtained with human coding and by the software alone were compared. Results for all four analyses produced automatically were comparable to published data on the manual interrater reliability of these procedures. Clinical decisions based on cutoff scores and productivity data were little affected by the use of automatic rather than human-generated analyses. These findings bode well for future clinical and research use of automatic language analysis software.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

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