Annotation and verification of sense pools in OntoNotes

Yu, Liang-Chih; Wu, Chung-Hsien; Chang, Ru-Yng; Liu, Chao-Hong; Hovy, Eduard

doi:10.1016/j.ipm.2009.11.002

Cited by 14 publications

(6 citation statements)

References 30 publications

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“…Among those studies was the OntoNotes project, which annotated a multilingual corpus for different levels of semantic structure in the text [25,44,45]. One of the annotation levels includes linking OntoNotes word senses to the Omega ontology [25,46,68]. Near-synonymous word sense pools were created by specialists who grouped sense distinctions from WordNet and dictionaries based on similar definitions.…”

Section: Annotation Tagging and Ontologies For Natural Language Proce...mentioning

confidence: 99%

Terminology and ontology development for semantic annotation: A use case on sepsis and adverse events

Yan

Gustad

Høvik

et al. 2023

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Annotations enrich text corpora and provide necessary labels for natural language processing studies. To reason and infer underlying implicit knowledge captured by labels, an ontology is needed to provide a semantically annotated corpus with structured domain knowledge. Utilizing a corpus of adverse event documents annotated for sepsis-related signs and symptoms as a use case, this paper details how a terminology and corresponding ontology were developed. The Annotated Adverse Event NOte TErminology (AAENOTE) represents annotated documents and assists annotators in annotating text. In contrast, the complementary Catheter Infection Indications Ontology (CIIO) is intended for clinician use and captures domain knowledge needed to reason and infer implicit information from data. The approach taken makes ontology development understandable and accessible to domain experts without formal ontology training.

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Section: Annotation Tagging and Ontologies For Natural Language Proce...mentioning

confidence: 99%

Terminology and ontology development for semantic annotation: A use case on sepsis and adverse events

Yan

Gustad

Høvik

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The PMI of x and y considers the number of times x and y occurred together, f(x, y), and the frequency of x, f(x), and the frequency of y, f(y). The PMI is computed using the following equation: The PMI does not demand the adjacency of x and y, and the researchers used the PMI with different window sizes, for example, Inkpen in [15] used a content window of size 2, whereas, Yu et al in [14,16] used a content window of size 4.…”

Section: Ows Importancementioning

confidence: 99%

“…The nouns' co-occurrences are represented by semantic relations instead of simply counting their occurrences. • Efficiency and precision improvement: the use of the OWS will reduce the number of processed terms in every single run of the synonyms extraction, which will participate in enhancing the efficiency (see Table 7, and Figure 15, 16). And, the semantic investigation of the text contents will participate in increasing the precision and provide more accurate synonyms (see Figure 9, 10, 11, 12 and Table 4, 5, 6).…”

Section: Research Aims and Contributionsmentioning

confidence: 99%

New Synonyms Extraction Model Based on a Novel Terms Weighting Scheme

Ababneh

2021

J. inf. organ. sci. (Online)

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The traditional statistical approach in synonyms extraction is time-consuming. It is necessary to develop a new method to improve the efficiency and accuracy. This research presents a new method in synonyms extraction called Noun Based Distinctive Verbs (NBDV) that replaces the traditional tf-idf weighting scheme with a new weighting scheme called the Orbit Weighing Scheme (OWS). The OWS links the nouns to their semantic space by examining the singular verbs in each context. The new method was compared with important models in the field such as the Skip-Gram, the Continuous Bag of Words, and the GloVe model. The NBDV model was manipulated over the Arabic and English languages and the results showed 47% Recall and 51% Precision in the dictionary-based evaluation and 57.5% Precision in the human experts’ evaluation. Comparing with the synonyms extraction based on tf.idf, the NBDV obtained 11% higher recall and 10% higher precision. Regarding the efficiency, we found that on average, the synonyms extraction of a single noun requires the process of 186 verbs and in 63% of the runs; the number of singular verbs was less than 200. It is concluded that the developed method is efficient and processes the single run in linear time.

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“…Language modeling approaches have been successfully used in many applications, such as grammar error correction [28], code-switching language processing [29], and lexical substitution [30][31][32]. For our task, a code-switched sentence generally has a higher probability of being found in a code-switching language model than in a noncode-switching one.…”

Section: Language Modeling Approachmentioning

confidence: 99%

Identification of Code-Switched Sentences and Words Using Language Modeling Approaches

Chien

et al. 2013

Mathematical Problems in Engineering

Self Cite

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Globalization and multilingualism contribute to code-switching—the phenomenon in which speakers produce utterances containing words or expressions from a second language. Processing code-switched sentences is a significant challenge for multilingual intelligent systems. This study proposes a language modeling approach to the problem of code-switching language processing, dividing the problem into two subtasks: the detection of code-switched sentences and the identification of code-switched words in sentences. A code-switched sentence is detected on the basis of whether it contains words or phrases from another language. Once the code-switched sentences are identified, the positions of the code-switched words in the sentences are then identified. Experimental results show that the language modeling approach achieved anF-measure of 80.43% and an accuracy of 79.01% for detecting Mandarin-Taiwanese code-switched sentences. For the identification of code-switched words, the word-based and POS-based models, respectively, achievedF-measures of 41.09% and 53.08%.

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Annotation and verification of sense pools in OntoNotes

Cited by 14 publications

References 30 publications

Terminology and ontology development for semantic annotation: A use case on sepsis and adverse events

Terminology and ontology development for semantic annotation: A use case on sepsis and adverse events

New Synonyms Extraction Model Based on a Novel Terms Weighting Scheme

Identification of Code-Switched Sentences and Words Using Language Modeling Approaches

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