Sentence Semantic Similarity Using Dependency Parsing

Vakare, Tanmay; Verma, Kshitij; Jain, Vedant

doi:10.1109/icccnt45670.2019.8944671

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Another work is done for the similarity of words on a limited PoS by using synsets in WordNet, to extend this lexical similarity on phrase level and sentence level computed with the help of word-level similarity [22]. Similarly, the lexical similarity computed for the source code by using string level matching [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Hindustani or Hindi vs. Urdu: A Computational Approach for the Exploration of Similarities Under Phonetic Aspects

Suffian¹,

Ahmed²,

Yaseen³

2020

IJACSA

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The semantic coexistence is the reason to adopt the language spoken by other people. In such human habitats, different languages share words typically known as loan words which appears not only as of the principal medium of enriching language vocabulary but also for creating influence upon each other for building stronger relationships and forming multilingualism. In this context, the spoken words are usually common but their writing scripts vary or the language may have become a digraphia. In this paper, we presented the similarities and relatedness between Hindi and Urdu (that are mutually intelligible and major languages of Indian sub-continent). In general, the method modifies edit-distance; and works in the fashion that instead of using alphabets from the words it uses articulatory features from the International Phonetic Alphabets (IPA) to get the phonetic edit distance. This paper also shows the results for the languages consonant under the method which quantifies the evidence that the Urdu and Hindi languages are 67.8% similar on average despite the script differences.

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Section: Literature Reviewmentioning

confidence: 99%

Hindustani or Hindi vs. Urdu: A Computational Approach for the Exploration of Similarities Under Phonetic Aspects

Suffian¹,

Ahmed²,

Yaseen³

2020

IJACSA

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“…Dependency parsing is to extract the dependency structure of a sentence that shows its grammatical structure and the relationships between "head" words and associated "dependent" words. Dependency parsing can provide the syntactic structure information for sentence, which can be used to enhance other Natural Language Processing task such as Named Entity Recognition (Vakare et al, 2019) and sentence Semantic Similarity (Jie et 2017). Existing in-domain dependency parsing model has achieved promising performance in the domains that have abundant labeled data such as news and magazines (Ma et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing

Lin¹,

Li²,

Li³

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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Unsupervised cross-domain dependency parsing is to accomplish domain adaptation for dependency parsing without using labeled data in target domain. Existing methods are often of the pseudo-annotation type, which generates data through self-annotation of the base model and performing iterative training. However, these methods fail to consider the change of model structure for domain adaptation. In addition, the structural information contained in the text cannot be fully exploited. To remedy these drawbacks, we propose a Semantics-Structure Adaptative Dependency Parser (SSADP), which accomplishes unsupervised cross-domain dependency parsing without relying on pseudo-annotation or data selection. In particular, we design two feature extractors to extract semantic and structural features respectively. For each type of features, a corresponding feature adaptation method is utilized to achieve domain adaptation to align the domain distribution, which effectively enhances the unsupervised crossdomain transfer capability of the model. We validate the effectiveness of our model by conducting experiments on the CODT1 and CTB9 respectively, and the results demonstrate that our model can achieve consistent performance improvement. Besides, we verify the structure transfer ability of the proposed model by introducing Weisfeiler-Lehman Test.

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Comparative Evaluation of Semantic Similarity Upon Sentential Text of Varied (Generic) Lengths

Dhagat¹,

Rawal²,

Soni³

2022

Lecture Notes in Electrical Engineering

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Sentence Semantic Similarity Using Dependency Parsing

Cited by 5 publications

References 4 publications

Hindustani or Hindi vs. Urdu: A Computational Approach for the Exploration of Similarities Under Phonetic Aspects

Hindustani or Hindi vs. Urdu: A Computational Approach for the Exploration of Similarities Under Phonetic Aspects

Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing

Comparative Evaluation of Semantic Similarity Upon Sentential Text of Varied (Generic) Lengths

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