---
# System prepended metadata

title: B4 & M1 Lecture 2020

---

# B4 & M1 Lecture 2020

* Supervisor: 1st-year doctor course students (小林, Kwon, Yukun, and Zhang)
* In this lecture, we read a book to understant the basic knowledge of NLP. B4 and M1 students present their part by the following order. M2 students and Doctor candidates advise to them. In this year, you should make your slide in English.

## Syllabus

### Outline
* In principle, B4 and M1 students must participate. Please make sure to contant the advisor of your part if you cannot attend the meeting due to a personal matter.

### Format
* We read **SLP-book edition 3** (currently drafted version) "**[SPEECH and LANGUAGE PROCESSING](https://web.stanford.edu/~jurafsky/slp3/)**"
    * The authors' slides are uploaded on this page: [support page by author](https://web.stanford.edu/~jurafsky/slp3/)
* Presenters draw up presentation slides (in English), and introduce to participant.
* You should appload your slide to [our shared directory](https://onedrive.live.com/?authkey=%21AFDvWFuTSQTAUwM&id=F66384EF4F2C7177%21200&cid=F66384EF4F2C7177) with file name \[Your name\]\_\[Chapter\]\_\[Section\].pptx. Note that you need to create your account on OneDrive for uploading slides.
* After uploading your slide, please share the url of the slide like [this](https://1drv.ms/p/s!AndxLE_vhGP2gUoDkOV2sihCLzsL?e=enpph1).

## Schedule

* Every Monday and Tuesday.

This schedule is temporary and can be changed as required.
*: finished

| Date | Presenter | Advisor | Chapter | Title | Section | Slides |
| ---- | --------- | ------- | ------- | ----- | ------- | ------ |
| 4/27 | 川本*   | 山田    | 03 | N-gram Language Models | 3-3.1 | [URL](https://1drv.ms/p/s!AndxLE_vhGP2gVdI_FOYfJkgq0t8?e=0N48rx) |
| 4/27 | 廣部   | 川原田  | 03 | N-gram Language Models | 3.2-3.4 | URL |
| 4/28 | Zuo*  | 竹下    | 03 | N-gram Language Models | 3.5-3.8  | URL |
| 4/28 | Wu*   | 川村    | 04 | Naive Bayes and Sentiment Classification | 4-4.3    | URL |
| 5/11 | 福島   | Li Jia | 04 | Naive Bayes and Sentiment Classification  | 4.4-4.10 | URL |
| 5/11 | 田代  | You    | 05 | Logistic Regression | 5-5.3   | URL |
| 5/12 | Rong  | Haitao | 05 | Logistic Regression | 5.4-5.7 | URL |
| 5/12 | Xiong | 山田    | 06 | Vector Semantics | 6-6.2 | URL |
| 5/18 | Ong   | 川原田  | 06 | Vector Semantics | 6.3-6.5 | URL |
| 5/18 | 藤田   | 竹下    | 07 | Neural Nets and Neural Language Models | 7-7.2 | URL |
| 5/19 | 田中   | 川村    | 07 | Neural Nets and Neural Language Models | 7.3-7.4 | URL |
| 5/19 | 川本 | Li Jia | 07 | Neural Nets and Neural Language Models | 7.5-7.6 | URL |
| 5/25 | 廣部 | You    | 08 | Part-of-Speech Tagging | 8-8.3 | URL |
| 5/25 | Zuo | Haitao | 08 | Part-of-Speech Tagging | 8.4 | URL |
| 5/26 | Wu | 山田    | 08 | Part-of-Speech Tagging | 8.5-8.8 | URL |
| 5/26 | 福島 | 川原田  | 09 | Sequence Processing with Recurrent Networks | 9-9.1 | URL |
| 6/1 | 田代 | 竹下    | 09 | Sequence Processing with Recurrent Networks | 9.2-9.3 | URL |
| 6/1 | Rong | 川村 | 09 | Sequence Processing with Recurrent Networks | 9.4-9.6 | URL |
| 6/2 | Xiong | Li Jia | 10 | Encoder-Decoder Models, Attention, and Contextual Embeddings | 10-10.3 | URL |
| 6/2 | Ong | You | 12 | Constituency Grammars | 12-12.2 | URL |
| 6/8 | 藤田 | Haitao | 12 | Constituency Grammars | 12.3 | URL |
| 6/8 | 田中 | 山田 | 12 | Constituency Grammars | 12.4-12.5 | URL |
| 6/9 | 川本 | 川原田 | 12 | Constituency Grammars | 12.6-12.7 | URL |
| 6/9 | 廣部 | 竹下 | 13 | Constituency Parsing   | 13-13.2 | URL |
| 6/16 | Zuo | 川村 | 13 | Constituency Parsing   | 13.3-13.4 | URL |
| 6/16 | Wu | Li Jia | 15 | Dependency Parsing | 13-13.3 | URL |
| 6/22 | 福島 | You | 15 | Dependency Parsing | 13.4 | URL |
| 6/22 | 田代 | Haitao | 15 | Dependency Parsing | 13.5-13.7 | URL |
