Social media platforms like twitter and facebook have be- come two of the
largest mediums used by people to express their views to- wards different
topics. Generation of such large user data has made NLP tasks like sentiment
analysis and opinion mining much more important. Using sarcasm in texts on
social media has become a popular trend lately. Using sarcasm reverses the
meaning and polarity of what is implied by the text which poses challenge for
many NLP tasks. The task of sarcasm detection in text is gaining more and more
importance for both commer- cial and security services. We present the first
English-Hindi code-mixed dataset of tweets marked for presence of sarcasm and
irony where each token is also annotated with a language tag. We present a
baseline su- pervised classification system developed using the same dataset
which achieves an average F-score of 78.4 after using random forest classifier
and performing 10-fold cross validation.

通过英 - 印混合码数据集，我们开发了一个基于随机森林分类器并进行了 10 折交叉验证的基线监督分类系统，该系统可以检测到社交媒体上的文字中的反讽，并标记每个标记的语言标签。该系统平均得分为 78.4%。

英印混合代码推文语料库用于讽刺检测

A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection

Prepositions are very common and very ambiguous, and understanding their
sense is critical for understanding the meaning of the sentence. Supervised
corpora for the preposition-sense disambiguation task are small, suggesting a
semi-supervised approach to the task. We show that signals from unannotated
multilingual data can be used to improve supervised preposition-sense
disambiguation. Our approach pre-trains an LSTM encoder for predicting the
translation of a preposition, and then incorporates the pre-trained encoder as
a component in a supervised classification system, and fine-tunes it for the
task. The multilingual signals consistently improve results on two
preposition-sense datasets.

本研究采用半监督方法来增强介词意义的自动消歧功能。通过在未标注的多语数据上进行预训练，利用一个 LSTM 编码器来预测介词的翻译，然后在一个监督分类系统中将其作为组成部分进行微调，结果表明这种多语信号能够显著提高两个介词意义数据集的结果表现。