Generating extractive sentiment summaries for natural language user queries on products

Few-shot knowledge graph completion (FKGC) task aims to infer missing entities or relations by using few-shot support instances in the knowledge graph. Existing FKGC methods focus on the learning of few-shot relation representations, which are obtained by aggregating the neighbor information of each entity. However, most of these models take the entity's neighbor relations and entities as the same hierarchy and do not make fine-grained distinctions, resulting in entity embeddings with low expressiveness, which may further decrease the quality of learned few-shot relation embeddings. Moreover, many of those models directly use the concatenation of the entity embeddings as the relation representations, and neglect the valuable interaction between relations. In this paper, we propose a fine-grained relational learning framework IDEAL for few-shot knowledge graph completion task. Specifically, we first propose a unique hierarchical attention encoder to aggregate the neighbor information of each entity from two levels, i.e., the entity-relation level and the relation-entity level. Then a relation recoding validator is proposed to formulate the interaction between different relations. Instead of obtaining the few-shot relation representations by using the entity embeddings, the relation recoding validator module aggregates the neighbor relations of each entity to encode the few-shot relation, which can reduce the over-dependence on specific entities in the few-shot relation encoding phase. The relation recoding module is also extended with respect to the excellent performance of the transformer in modeling sequence information. We then introduce a transformer encoder to extract underlying and valuable sequence information between relations. Extensive experiments are conducted on two datasets, i.e., NELL and Wiki. The experimental results demonstrate that our model outperforms state-of-the-art FKGC methods. Besides, we devise the ablation study to demonstrate the effectiveness of each key component. The case study also shows the interpretability of our model intuitively.

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A method of named entity recognition for Tigrinya

Yohannes

Amagasa

2022

SIGAPP Appl. Comput. Rev.

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This paper proposes a method for Named-Entity Recognition (NER) for a low-resource language, Tigrinya, using a pre-trained language model. Tigrinya is a morphologically rich, although one of the underrepresented in the field of NLP. This is mainly due to the limited amount of annotated data available. To address this problem, we present the first publicly available datasets of NER for Tigrinya containing two versions, namely, (V1 and V2) annotated manually. The V1 and V2 datasets contain 69,309 and 40,627 tokens, respectively, where the annotations are based on the CoNLL 2003 Beginning, Inside, and Outside (BIO) tagging schema. Specifically, we develop a new pre-trained language model for Tigrinya based on RoBERTa, which we refer to as TigRoBERTa. Our model is then fine-tuned on down-stream tasks on a more specific target NER and POS tasks with limited data. Finally, we further enhance the model performance by applying semi-supervised self-training using unlabeled data. The experimental results show that the method achieved 84% F1-score for NER and 92% accuracy for POS tagging, which is better than or comparable to the baseline method based on the CNN-BiLSTM-CRF.

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Generating extractive sentiment summaries for natural language user queries on products

Cited by 5 publications

References 25 publications

Feature Extraction from Text using Grasshopper optimization algorithm for identifying hate speech

Feature Extraction from Text using Grasshopper optimization algorithm for identifying hate speech

Fine-grained relational learning for few-shot knowledge graph completion

A method of named entity recognition for Tigrinya

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