Meaning in Language. An Introduction to Semantics and Pragmatics

Ardila, Juan Antonio Garrido

doi:10.1016/j.pragma.2011.03.014

Cited by 28 publications

(43 citation statements)

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“…Answer to Q1: A “good” vector semantic model should find a candidate y that is “semantically similar” to the target x = Romeo . The candidate y with the highest cosine for the CBOW model is you : The terms you and Romeo are near-synonyms, that is “interchangeable in some contexts” [ 38 ]. Hence, the answer to Q1 is “yes.”…”

Section: Methodsmentioning

confidence: 99%

Semantic Deep Learning: Prior Knowledge and a Type of Four-Term Embedding Analogy to Acquire Treatments for Well-Known Diseases

Casteleiro

Diz²,

Maroto

et al. 2020

JMIR Med Inform

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Background How to treat a disease remains to be the most common type of clinical question. Obtaining evidence-based answers from biomedical literature is difficult. Analogical reasoning with embeddings from deep learning (embedding analogies) may extract such biomedical facts, although the state-of-the-art focuses on pair-based proportional (pairwise) analogies such as man:woman::king:queen (“queen = −man +king +woman”). Objective This study aimed to systematically extract disease treatment statements with a Semantic Deep Learning (SemDeep) approach underpinned by prior knowledge and another type of 4-term analogy (other than pairwise). Methods As preliminaries, we investigated Continuous Bag-of-Words (CBOW) embedding analogies in a common-English corpus with five lines of text and observed a type of 4-term analogy (not pairwise) applying the 3CosAdd formula and relating the semantic fields person and death: “dagger = −Romeo +die +died” (search query: −Romeo +die +died). Our SemDeep approach worked with pre-existing items of knowledge (what is known) to make inferences sanctioned by a 4-term analogy (search query −x +z1 +z2) from CBOW and Skip-gram embeddings created with a PubMed systematic reviews subset (PMSB dataset). Stage1: Knowledge acquisition. Obtaining a set of terms, candidate y, from embeddings using vector arithmetic. Some n-gram pairs from the cosine and validated with evidence (prior knowledge) are the input for the 3cosAdd, seeking a type of 4-term analogy relating the semantic fields disease and treatment. Stage 2: Knowledge organization. Identification of candidates sanctioned by the analogy belonging to the semantic field treatment and mapping these candidates to unified medical language system Metathesaurus concepts with MetaMap. A concept pair is a brief disease treatment statement (biomedical fact). Stage 3: Knowledge validation. An evidence-based evaluation followed by human validation of biomedical facts potentially useful for clinicians. Results We obtained 5352 n-gram pairs from 446 search queries by applying the 3CosAdd. The microaveraging performance of MetaMap for candidate y belonging to the semantic field treatment was F-measure=80.00% (precision=77.00%, recall=83.25%). We developed an empirical heuristic with some predictive power for clinical winners, that is, search queries bringing candidate y with evidence of a therapeutic intent for target disease x. The search queries -asthma +inhaled_corticosteroids +inhaled_corticosteroid and -epilepsy +valproate +antiepileptic_drug were clinical winners, finding eight evidence-based beneficial treatments. Conclusions Extracting treatments with therapeutic intent by analogical reasoning from embeddings (423K n-grams from the PMSB dataset) is an ambitious goal. Our SemDeep approach is knowledge-based, underpinned by embedding analogies that exploit prior knowledge. Biomedical facts from embedding analogies (4-term type, not pairwise) are potentially useful for clinicians. The heuristic offers a practical way to discover beneficial treatments for well-known diseases. Learning from deep learning models does not require a massive amount of data. Embedding analogies are not limited to pairwise analogies; hence, analogical reasoning with embeddings is underexploited.

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Section: Methodsmentioning

confidence: 99%

Semantic Deep Learning: Prior Knowledge and a Type of Four-Term Embedding Analogy to Acquire Treatments for Well-Known Diseases

Casteleiro

Diz²,

Maroto

et al. 2020

JMIR Med Inform

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“…Identity relation is considered as an axis of the paradigmatic lexical-sense relations. According to Cruse (2000), this relation involves the study of synonymy, i.e., the sameness of meaning. Identity relation investigates whether two or more lexical items are close in meaning, i.e., whether they are synonyms or not.…”

Section: Identity Relationmentioning

confidence: 99%

“…

between words or predicates". "Paradigmatic relations are those into which a linguistic unit enters through being contrasted or substitutable, in particular environment, with other similar units", (Palmer 1997:67).To put it another way, a paradigmatic relation is one in which one lexical unit may be substituted by another (Geisler, 2011).Lexical / sense relations have been studied by different semanticists, but Cruse's (2000) is the most comprehensive of these accounts. Paradigmatic sense relations are divided into two broad categories: those that indicate identity and inclusion between word meanings, and those that express opposition and exclusion between word meanings.

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A Paradigmatic Lexical Relation Study of Analysing Entailment in Identity and Inclusion Relations

Taher

Salih²

2023

KOYA UNIV J HUM SOC SCI

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While paradigmatic lexical/sense relations are traditionally used to analyse the relations between lexical items, the present article adopts a descriptive qualitative method, based on Cruse’s (2000) classification of lexical/sense relations, to find the identity and inclusion relations of two lexical items in two different texts by using the logical entailment relation. The article aims at finding out whether or not lexical items can be substituted by another without changing their meaning; the meaning of one lexical item is included in the meaning of the other; and the validity of these relations in one context can be tested as entailment relations. The study concludes that entailment has a context-bound relation with regard to identity and inclusion relations. Entailment occurs freely with propositional synonymy. Although the two lexical items in near synonymy are close in meanings or related, entailment relation cannot be attained. Entailment in hyponymy is a unilateral relation. i.e., the meaning of one hyponym entails the meaning of the superordinate, but the meaning of the superordinate does not entail the meaning of each hyponym. Entailment in meronymy relation stands if the relation is between patronym to holonym, i.e., the entailment relation does not stand under holonym to partonym.

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“…Therefore, we may know what the words mean, to find out what kind of product or service is needed. There is a core meaning (Ardila, 2011) which is preserved and recycled. The novelty proposed by this research is that the interpretation of meaning by advertising can be found by linguistic application simply by finding the function and type of meaning.…”

Section: Inroductionmentioning

confidence: 99%

Semantic Analysis on Online Apple Advertisements

Basri

2022

RDT

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This research is entitled A Semantic Analysis on Online Apple Advertisements. It is a study of the analysis of meaning in online advertisements contained on the official Apple website. The researchers examine the type of meaning and the function of associative meaning with the aim of finding its relationship with the company’s advertising and sales activities. This research uses descriptive qualitative as a research method. From this analysis, the researchers found 10 data that matched the research criteria and considered as the latest data to complete the research. Connotation becomes the most dominant type of meaning used. As for the function of speech whose meaning is adapted to the context of the ad sentence, assertive and expressive are the most commonly used because it displays facts succinctly. Keywords: advertisements, apple, types of meaning

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Meaning in Language. An Introduction to Semantics and Pragmatics

Cited by 28 publications

References 1 publication

Semantic Deep Learning: Prior Knowledge and a Type of Four-Term Embedding Analogy to Acquire Treatments for Well-Known Diseases

Semantic Deep Learning: Prior Knowledge and a Type of Four-Term Embedding Analogy to Acquire Treatments for Well-Known Diseases

A Paradigmatic Lexical Relation Study of Analysing Entailment in Identity and Inclusion Relations

Semantic Analysis on Online Apple Advertisements

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