3,136 research outputs found
A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases
Deep language models learning a hierarchical representation proved to be a
powerful tool for natural language processing, text mining and information
retrieval. However, representations that perform well for retrieval must
capture semantic meaning at different levels of abstraction or context-scopes.
In this paper, we propose a new method to generate multi-resolution word
embeddings that represent documents at multiple resolutions in terms of
context-scopes. In order to investigate its performance,we use the Stanford
Question Answering Dataset (SQuAD) and the Question Answering by Search And
Reading (QUASAR) in an open-domain question-answering setting, where the first
task is to find documents useful for answering a given question. To this end,
we first compare the quality of various text-embedding methods for retrieval
performance and give an extensive empirical comparison with the performance of
various non-augmented base embeddings with and without multi-resolution
representation. We argue that multi-resolution word embeddings are consistently
superior to the original counterparts and deep residual neural models
specifically trained for retrieval purposes can yield further significant gains
when they are used for augmenting those embeddings
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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