Page: 1 2 3 4 5 6 7 8... 554
61 |
Automatic Dialect Density Estimation for African American English ...
|
|
|
|
BASE
|
|
Show details
|
|
62 |
Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for Hate Speech Detection ...
|
|
|
|
BASE
|
|
Show details
|
|
63 |
Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition ...
|
|
|
|
BASE
|
|
Show details
|
|
65 |
End-to-end contextual asr based on posterior distribution adaptation for hybrid ctc/attention system ...
|
|
|
|
BASE
|
|
Show details
|
|
67 |
Large-scale Bilingual Language-Image Contrastive Learning ...
|
|
|
|
BASE
|
|
Show details
|
|
68 |
Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems ...
|
|
|
|
BASE
|
|
Show details
|
|
69 |
Semantic properties of English nominal pluralization: Insights from word embeddings ...
|
|
|
|
BASE
|
|
Show details
|
|
71 |
Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns ...
|
|
|
|
Abstract:
Extracting cause-effect entities from medical literature is an important task in medical information retrieval. A solution for solving this task can be used for compilation of various causality relations, such as, causality between disease and symptoms, between medications and side effects, between genes and diseases, etc. Existing solutions for extracting cause-effect entities work well for sentences where the cause and the effect phrases are name entities, single-word nouns, or noun phrases consisting of two to three words. Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences. Partial extraction of cause and effect entities conveys poor quality, non informative, and often, contradictory facts, comparing to the one intended in the given sentence. In this work, we solve this problem by ... : 22 pages without comment ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
|
|
URL: https://dx.doi.org/10.48550/arxiv.2203.06592 https://arxiv.org/abs/2203.06592
|
|
BASE
|
|
Hide details
|
|
72 |
NorDiaChange: Diachronic Semantic Change Dataset for Norwegian ...
|
|
|
|
BASE
|
|
Show details
|
|
73 |
Toxicity Detection for Indic Multilingual Social Media Content ...
|
|
|
|
BASE
|
|
Show details
|
|
74 |
Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks ...
|
|
|
|
BASE
|
|
Show details
|
|
75 |
SciNLI: A Corpus for Natural Language Inference on Scientific Text ...
|
|
|
|
BASE
|
|
Show details
|
|
76 |
SHAS: Approaching optimal Segmentation for End-to-End Speech Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
77 |
A New Framework for Fast Automated Phonological Reconstruction Using Trimmed Alignments and Sound Correspondence Patterns ...
|
|
|
|
BASE
|
|
Show details
|
|
78 |
Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinese ...
|
|
|
|
BASE
|
|
Show details
|
|
79 |
Learning to pronounce as measuring cross-lingual joint orthography-phonology complexity ...
|
|
|
|
BASE
|
|
Show details
|
|
80 |
The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking ...
|
|
|
|
BASE
|
|
Show details
|
|
Page: 1 2 3 4 5 6 7 8... 554
|
|