310 research outputs found
Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis
Document-level Sentiment Analysis (DSA) is more challenging due to vague
semantic links and complicate sentiment information. Recent works have been
devoted to leveraging text summarization and have achieved promising results.
However, these summarization-based methods did not take full advantage of the
summary including ignoring the inherent interactions between the summary and
document. As a result, they limited the representation to express major points
in the document, which is highly indicative of the key sentiment. In this
paper, we study how to effectively generate a discriminative representation
with explicit subject patterns and sentiment contexts for DSA. A Hierarchical
Interaction Networks (HIN) is proposed to explore bidirectional interactions
between the summary and document at multiple granularities and learn
subject-oriented document representations for sentiment classification.
Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining
the HIN with sentiment label information to learn a more sentiment-aware
document representation. We extensively evaluate our proposed models on three
public datasets. The experimental results consistently demonstrate the
effectiveness of our proposed models and show that HIN-SR outperforms various
state-of-the-art methods.Comment: 17 pages, accepted by ECML-PKDD 202
End-to-End Entity Detection with Proposer and Regressor
Named entity recognition is a traditional task in natural language
processing. In particular, nested entity recognition receives extensive
attention for the widespread existence of the nesting scenario. The latest
research migrates the well-established paradigm of set prediction in object
detection to cope with entity nesting. However, the manual creation of query
vectors, which fail to adapt to the rich semantic information in the context,
limits these approaches. An end-to-end entity detection approach with proposer
and regressor is presented in this paper to tackle the issues. First, the
proposer utilizes the feature pyramid network to generate high-quality entity
proposals. Then, the regressor refines the proposals for generating the final
prediction. The model adopts encoder-only architecture and thus obtains the
advantages of the richness of query semantics, high precision of entity
localization, and easiness of model training. Moreover, we introduce the novel
spatially modulated attention and progressive refinement for further
improvement. Extensive experiments demonstrate that our model achieves advanced
performance in flat and nested NER, achieving a new state-of-the-art F1 score
of 80.74 on the GENIA dataset and 72.38 on the WeiboNER dataset
A Survey on Semantic Processing Techniques
Semantic processing is a fundamental research domain in computational
linguistics. In the era of powerful pre-trained language models and large
language models, the advancement of research in this domain appears to be
decelerating. However, the study of semantics is multi-dimensional in
linguistics. The research depth and breadth of computational semantic
processing can be largely improved with new technologies. In this survey, we
analyzed five semantic processing tasks, e.g., word sense disambiguation,
anaphora resolution, named entity recognition, concept extraction, and
subjectivity detection. We study relevant theoretical research in these fields,
advanced methods, and downstream applications. We connect the surveyed tasks
with downstream applications because this may inspire future scholars to fuse
these low-level semantic processing tasks with high-level natural language
processing tasks. The review of theoretical research may also inspire new tasks
and technologies in the semantic processing domain. Finally, we compare the
different semantic processing techniques and summarize their technical trends,
application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN
1566-2535. The equal contribution mark is missed in the published version due
to the publication policies. Please contact Prof. Erik Cambria for detail
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