2,506 research outputs found
Knowledge-based Query Expansion in Real-Time Microblog Search
Since the length of microblog texts, such as tweets, is strictly limited to
140 characters, traditional Information Retrieval techniques suffer from the
vocabulary mismatch problem severely and cannot yield good performance in the
context of microblogosphere. To address this critical challenge, in this paper,
we propose a new language modeling approach for microblog retrieval by
inferring various types of context information. In particular, we expand the
query using knowledge terms derived from Freebase so that the expanded one can
better reflect users' search intent. Besides, in order to further satisfy
users' real-time information need, we incorporate temporal evidences into the
expansion method, which can boost recent tweets in the retrieval results with
respect to a given topic. Experimental results on two official TREC Twitter
corpora demonstrate the significant superiority of our approach over baseline
methods.Comment: 9 pages, 9 figure
Asca: less audio data is more insightful
Audio recognition in specialized areas such as birdsong and submarine
acoustics faces challenges in large-scale pre-training due to the limitations
in available samples imposed by sampling environments and specificity
requirements. While the Transformer model excels in audio recognition, its
dependence on vast amounts of data becomes restrictive in resource-limited
settings. Addressing this, we introduce the Audio Spectrogram Convolution
Attention (ASCA) based on CoAtNet, integrating a Transformer-convolution hybrid
architecture, novel network design, and attention techniques, further augmented
with data enhancement and regularization strategies. On the BirdCLEF2023 and
AudioSet(Balanced), ASCA achieved accuracies of 81.2% and 35.1%, respectively,
significantly outperforming competing methods. The unique structure of our
model enriches output, enabling generalization across various audio detection
tasks. Our code can be found at https://github.com/LeeCiang/ASCA.Comment: 6 pages,3 figure
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