Social media data such as Twitter messages ("tweets") pose a particular
challenge to NLP systems because of their short, noisy, and colloquial nature.
Tasks such as Named Entity Recognition (NER) and syntactic parsing require
highly domain-matched training data for good performance. To date, there is no
complete training corpus for both NER and syntactic analysis (e.g., part of
speech tagging, dependency parsing) of tweets. While there are some publicly
available annotated NLP datasets of tweets, they are only designed for
individual tasks. In this study, we aim to create Tweebank-NER, an English NER
corpus based on Tweebank V2 (TB2), train state-of-the-art (SOTA) Tweet NLP
models on TB2, and release an NLP pipeline called Twitter-Stanza. We annotate
named entities in TB2 using Amazon Mechanical Turk and measure the quality of
our annotations. We train the Stanza pipeline on TB2 and compare with
alternative NLP frameworks (e.g., FLAIR, spaCy) and transformer-based models.
The Stanza tokenizer and lemmatizer achieve SOTA performance on TB2, while the
Stanza NER tagger, part-of-speech (POS) tagger, and dependency parser achieve
competitive performance against non-transformer models. The transformer-based
models establish a strong baseline in Tweebank-NER and achieve the new SOTA
performance in POS tagging and dependency parsing on TB2. We release the
dataset and make both the Stanza pipeline and BERTweet-based models available
"off-the-shelf" for use in future Tweet NLP research. Our source code, data,
and pre-trained models are available at:
https://github.com/social-machines/TweebankNLP.

本研究旨在创建 Tweebank-NER，一个基于 Tweebank V2 的英文 NER 语料库，用 TB2 训练最先进的 Tweet NLP 模型，并发布名为 Twitter-Stanza 的 NLP 管道。

在 Tweebank 语料库上进行命名实体识别注释，并建立用于社交媒体分析的 NLP 模型

Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis

Social media data can be a very salient source of information during crises.
User-generated messages provide a window into people's minds during such times,
allowing us insights about their moods and opinions. Due to the vast amounts of
such messages, a large-scale analysis of population-wide developments becomes
possible. In this paper, we analyze Twitter messages (tweets) collected during
the first months of the COVID-19 pandemic in Europe with regard to their
sentiment. This is implemented with a neural network for sentiment analysis
using multilingual sentence embeddings. We separate the results by country of
origin, and correlate their temporal development with events in those
countries. This allows us to study the effect of the situation on people's
moods. We see, for example, that lockdown announcements correlate with a
deterioration of mood in almost all surveyed countries, which recovers within a
short time span.

本论文使用多语言句子嵌入的神经网络对欧洲 COVID-19 疫情爆发期间收集的 Twitter 消息进行情感分析，并将结果按来源国分离，通过与国家事件的相关性进行时间性发展的相关性分析，研究疫情对人们情绪的影响。

COVID-19 疫情期间欧洲推特跨语言情感分析

Cross-language sentiment analysis of European Twitter messages duringthe  COVID-19 pandemic

We present a probabilistic model of events in continuous time in which each
event triggers a Poisson process of successor events. The ensemble of observed
events is thereby modeled as a superposition of Poisson processes. Efficient
inference is feasible under this model with an EM algorithm. Moreover, the EM
algorithm can be implemented as a distributed algorithm, permitting the model
to be applied to very large datasets. We apply these techniques to the modeling
of Twitter messages and the revision history of Wikipedia.

提出了一种连续时间事件的概率模型，采用触发后继事件泊松过程的方法对观察到的事件进行建模，并使用 EM 算法进行高效推理，可以作为分布式算法实现，可用于模拟推特消息和维基百科的修订历史。