graph-based techniques have gained traction for representing and analyzing data in various naturallanguageprocessing (NLP) tasks. Knowledge graph-basedlanguage representation models have shown promising results in ...
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A Knowledge graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse naturallanguageprocessing (NLP) tasks where knowledge is required. the need t...
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Withthe development of artificial intelligence and naturallanguageprocessing, speech emotion analysis technology has gradually gained widespread attention as an important research field. the complexity of speech em...
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ISBN:
(数字)9798350386943
ISBN:
(纸本)9798350386950
Withthe development of artificial intelligence and naturallanguageprocessing, speech emotion analysis technology has gradually gained widespread attention as an important research field. the complexity of speech emotion remains a challenging task, and traditional neural networks are limited by the construction of feature vectors, resulting in lower accuracy. In this paper, an end-to-end graph neural network is proposed. Firstly, the speech signals are segmented into spectra segments, taking into account that adjacent samples have similar distribution characteristics, thereby constructing a KNN graphbased on spectral samples. then, through training the dynamic graph attention network, and finally, aggregating the classification categories through fully connected layers, the experimental results show higher classification accuracy on the open-source speech emotion dataset EMODB.
Derivational nouns are widely used in Sanskrit corpora and is a prevalent means of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised ...
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Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many naturallanguage understanding (NLU) applications has a challenge where unlabeled data is...
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Background the recognition of pharmacological substances, compounds and proteins is essential for biomedical relation extraction, knowledge graph construction, drug discovery, as well as medical question answering. Al...
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Background the recognition of pharmacological substances, compounds and proteins is essential for biomedical relation extraction, knowledge graph construction, drug discovery, as well as medical question answering. Although considerable efforts have been made to recognize biomedical entities in English texts, to date, only few limited attempts were made to recognize them from biomedical texts in other languages. PharmaCoNER is a named entity recognition challenge to recognize pharmacological entities from Spanish texts. Because there are currently abundant resources in the field of naturallanguageprocessing, how to leverage these resources to the PharmaCoNER challenge is a meaningful study. methods Inspired by the success of deep learning withlanguage models, we compare and explore various representative BERT models to promote the development of the PharmaCoNER task. Results the experimental results show that deep learning withlanguage models can effectively improve model performance on the PharmaCoNER dataset. Our method achieves state-of-the-art performance on the PharmaCoNER dataset, with a max F1-score of 92.01%. Conclusion For the BERT models on the PharmaCoNER dataset, biomedical domain knowledge has a greater impact on model performance than the native language (i.e., Spanish). the BERT models can obtain competitive performance by using WordPiece to alleviate the out of vocabulary limitation. the performance on the BERT model can be further improved by constructing a specific vocabulary based on domain knowledge. Moreover, the character case also has a certain impact on model performance.
this paper describes the framework proposed by the UNIMIB Team for the task of Named Entity Recognition and Linking of Italian tweets (NEEL-IT). the proposed pipeline, which represents an entry level system, is compos...
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this paper describes the framework proposed by the UNIMIB Team for the task of Named Entity Recognition and Linking of Italian tweets (NEEL-IT). the proposed pipeline, which represents an entry level system, is composed of three main steps: (1) Named Entity Recognition using Conditional Random Fields, (2) Named Entity Linking by considering both Supervised and Neural-Network language models, and (3) NIL clustering by using a graph-based approach.
Bootstrapping has recently become the focus of much attention in naturallanguageprocessing to reduce labeling cost. In bootstrapping, unlabeled instances can be harvested from the initial labeled "seed" se...
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the proceedings contain 16 papers. the topics discussed include: graph-based clustering for computational linguistics: a survey;towards the automatic creation of a wordnet from a term-based lexical network;an investig...
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ISBN:
(纸本)1932432779
the proceedings contain 16 papers. the topics discussed include: graph-based clustering for computational linguistics: a survey;towards the automatic creation of a wordnet from a term-based lexical network;an investigation on the influence of frequency on the lexical organization of verbs;robust and efficient page rank for word sense disambiguation;hierarchical spectral partitioning of bipartite graphs to cluster dialects and identify distinguishing features;a character-based intersection graph approach to linguistic phylogeny;spectral approaches to learning in the graph domain;and cross-lingual comparison between distributionally determined word similarity networks.
Word Sense Induction (WSI) is an unsupervised approach for learning the multiple senses of a word. graph-based approaches to WSI frequently represent word co-occurrence as a graph and use the statistical properties of...
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