This paper discusses the experiments carried out by us at Jadavpur University
as part of the participation in ICON 2015 task: POS Tagging for Code-mixed
Indian Social Media Text. The tool that we have developed for the task is based
on Trigram Hidden Markov Model that utilizes information from dictionary as
well as some other word level features to enhance the observation probabilities
of the known tokens as well as unknown tokens. We submitted runs for
Bengali-English, Hindi-English and Tamil-English Language pairs. Our system has
been trained and tested on the datasets released for ICON 2015 shared task: POS
Tagging For Code-mixed Indian Social Media Text. In constrained mode, our
system obtains average overall accuracy (averaged over all three language
pairs) of 75.60% which is very close to other participating two systems (76.79%
for IIITH and 75.79% for AMRITA_CEN) ranked higher than our system. In
unconstrained mode, our system obtains average overall accuracy of 70.65% which
is also close to the system (72.85% for AMRITA_CEN) which obtains the highest
average overall accuracy.

本文描述我们在 Jadavpur 大学参加 ICON 2015 任务 - 对混合码印度社交媒体文本进行 POS 标注的实验，并开发了一种基于三元隐马尔可夫模型的工具，该模型利用字典以及其他一些单词级别的特征来增强已知和未知代币的观察概率。 在受控模式下，我们的系统平均整体准确度（在所有三种语言对上取平均值）为 75.60％，这非常接近排名高于我们系统的其他两个系统（IIITH 为 76.79％，AMRITA_CEN 为 75.79％）。 在不受约束的模式下，我们的系统获得了平均整体准确度为 70.65％，这也接近获得最高平均整体准确度的系统（AMRITA_CEN 为 72.85％）。

ICON 2015 上面向混合语印度社交媒体文本的词性标注

Part-of-Speech Tagging for Code-mixed Indian Social Media Text at ICON  2015

This paper presents the experiments carried out by us at Jadavpur University
as part of the participation in FIRE 2015 task: Entity Extraction from Social
Media Text - Indian Languages (ESM-IL). The tool that we have developed for the
task is based on Trigram Hidden Markov Model that utilizes information like
gazetteer list, POS tag and some other word level features to enhance the
observation probabilities of the known tokens as well as unknown tokens. We
submitted runs for English only. A statistical HMM (Hidden Markov Models) based
model has been used to implement our system. The system has been trained and
tested on the datasets released for FIRE 2015 task: Entity Extraction from
Social Media Text - Indian Languages (ESM-IL). Our system is the best performer
for English language and it obtains precision, recall and F-measures of 61.96,
39.46 and 48.21 respectively.

本文研究使用三元隐马尔科夫模型开发工具进行实体提取，基于统计学的 HMM 模型在印度语言的社交媒体文本中表现最佳，其中英文的精度为 61.96%。