【CE314】Computer Science NLP

Deadline: Please follow deadline on FASER

Build a text classifier on the IMDB sentiment classification dataset, you can use any classification method, but you must training your model on the first 40000 instances and testing your model on the last 10000 instances. The IMDB dataset will be uploaded on the moodle page for you to download.

Your code should include:

1: Read the file, incorporate the instances into the training set and testing set.

2: Pre-processing the text, you can choose whether you need stemming, removing stop words, removing non-alphabetical words. (Not all classification models need this step, it is OK if you think your model can perform better without this step, and you can give some justification in the report.)

3: Analysing the feature of the training set, report the linguistic features of the training dataset.

4: Build a text classification model, train your model on the training set and test your model on the test set.

5: Summarize the performance of your model (You can gain additional marks if you have some graph visualization).

6: (Optional) You can speculate how you can improve your works based on your proposed model.

After you build such a model and test on the test set, you should write a report (no longer than three pages in A4, with Arial 11 fonts) to summarize your work.

(You can use the existing algorithms on github or kaggle, but you must not directly copy and paste their code!

However, you are not allowed to use the Naïve Bayes algorithm and VADER classifier, which practiced in Lab 4)

Suggestion: some bonus points:

Have necessary comments on your code

Have proper reference on your report

Have graph visualization on your report

Investigate more evaluation methods, like not only show the P R F score, but also run multiple times and show the standard derivation on P R F (I am sure you can find more evaluation methods.)

Write your report like a mini-conference paper (you can learn from this paper:

  • Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical Attention Networks for Document Classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages 1480--1489, San Diego, California. Association for Computational Linguistics.
相关推荐
张较瘦_1 小时前
[论文阅读] 人工智能 + 软件工程 | 需求获取访谈中LLM生成跟进问题研究:来龙去脉与创新突破
论文阅读·人工智能
一 铭2 小时前
AI领域新趋势:从提示(Prompt)工程到上下文(Context)工程
人工智能·语言模型·大模型·llm·prompt
麻雀无能为力5 小时前
CAU数据挖掘实验 表分析数据插件
人工智能·数据挖掘·中国农业大学
时序之心5 小时前
时空数据挖掘五大革新方向详解篇!
人工智能·数据挖掘·论文·时间序列
.30-06Springfield6 小时前
人工智能概念之七:集成学习思想(Bagging、Boosting、Stacking)
人工智能·算法·机器学习·集成学习
说私域7 小时前
基于开源AI智能名片链动2+1模式S2B2C商城小程序的超级文化符号构建路径研究
人工智能·小程序·开源
永洪科技7 小时前
永洪科技荣获商业智能品牌影响力奖,全力打造”AI+决策”引擎
大数据·人工智能·科技·数据分析·数据可视化·bi
shangyingying_17 小时前
关于小波降噪、小波增强、小波去雾的原理区分
人工智能·深度学习·计算机视觉
书玮嘎8 小时前
【WIP】【VLA&VLM——InternVL系列】
人工智能·深度学习
猫头虎9 小时前
猫头虎 AI工具分享:一个网页抓取、结构化数据提取、网页爬取、浏览器自动化操作工具:Hyperbrowser MCP
运维·人工智能·gpt·开源·自动化·文心一言·ai编程