Literature Review

The literature review is a paper that reviews a subfield of NLP of your choice.

To ensure some intellectual diversity and depth of literature search, your review must cover at least 12 resesarch papers, and there must be at least 2 papers in each decade since 1990, and 2 papers from before 1990. That is, at least 2 papers in each bucket <=1989, 1990-1999, 2000-2009, and 2010-2019.

It can sometimes be harder to find older NLP papers that are also good/relevant. But machine learning, statistics, and linguistics are substantially older disciplines and nearly all NLP work builds on their ideas. Not all 12 of your reviewed papers have to be NLP -- in fact, it's fine if a majority aren't NLP, as long as they're helping the reader understand the overall NLP topic.

If you review a full length book, that counts as 3 papers.

We generally expect it to be 8-15 pages long, not including the references list at the end. You should use the ACL style files (download the LaTeX (or even Word) template from the ACL CFP).

Literature reviews must be completed individually.

Your review should not merely describe the papers, but also synthesize, organize, and relate them to one another and the broader literature in NLP, and ideally also ML and linguistics. It can be done either individually or in a group of two.

Here are two excellent examples of papers that were orginally lit reviews for a course:

Kamanth and Das (2019), "A Survey on Semantic Parsing," was originally a lit review for this course! Its authors worked on it more and successfully submitted it to AKBC the next year. They got these pretty positive reviews.
Das and Martins (2007), "A Survey on Automatic Text Summarization," was originally written for a similar lit review class assignment, and has been cited at least a dozen of times since.

Other examples, with more synthesis so they aren't purely literature reviews, include Turney and Pantel (2010)'s survey paper on distributional semantics, and Eisenstein (2013)'s survey/position paper on NLP for internet "bad language."

There are different ways to structure a literature review. Typically, you should have something like:

An introduction section that explains what the area is and the motivation to study it --- why is it an interesting area of research, and why should the reader care?
A main body, typically divided into several sections, that describes previous work and specific research papers. The most boring way to structure this is as a long list of papers with a paragraph describing each. That's OK when you're writing notes for yourself, but it's better to do some synthesis as well. Try to group papers by common themes, methods, datasets, or assumptions. What is similar and different among them? Did the body of research change over the years? Do different areas of research approach it differently?
A discussion and (potentially brief) conclusion, which sums up the main points you made. This may be a good place to discuss interesting possibilities for future work, or class projects!

Also make sure to:

Have a title, author name, and date.
Have a properly and consistently formatted references list. There are different standards to do this (for example, opinions differ whether the page number is that relevant any more; in this class we don't care), but make sure there is enough key information for others to be able to find the paper, know the exact version you are referring to, and to make a quick determination of its credibility. That means at least: authors, article title, name of publication or venue, date.

Research paper reading

When reading and discussing a research paper, here are some things to write up, or make sure you can answer to your satisfaction:

What is the authors' research question? What problem are they trying to solve? Distill it to one sentence. Sometimes the paper authors do not concisely state this, so it is up to you to figure this out.
What is a concrete example of the NLP task, or type of data or linguistic phenomenon, that the paper is about? Sometimes the paper authors do not do a good job at this; you can better understand what they're doing by thinking or discussing specific examples.
Describe what the authors did, their results, and any implications it may have.
Discussion: what questions did you have? What do you wish or want to be clarified? What do you think of the work and are there suggestions or ideas for future work?

It's OK to explicitly use questions like these when structuring your reading assignment writeups or your initial notes to yourself. For your actual literature review document, it may be awkward or clunky to explicitly structure your discussion of each paper with the above questions, but whatever you write should implicitly address these questions.

General tips for researching the literature

Use Google Scholar or Semantic Scholar to find related papers, and papers that cite a particular paper you've found.
Always look at a paper's references list and try to find the most interesting-looking, or most-cited previous papers. Keep a text file or bookmarks of references that look promising, to check back on later.
Look at other papers written by the paper's authors, especially the more senior ones who may have worked in this area for a while. Sometimes the paper you're looking at is less interesting or relevant than a related one written by one of itst authors.
Learn the important terms in the research area, which will help find more relevant or interesting papers -- for example, if you keep searching for "chatbots", you might find there is a field called "dialog systems" which is extremely relevant. Find the names of journals, conferences, and workshops in the field, as well as particular people who do lots of resesarch in the area; you can use all of this to find more related work.
Make sure to skim papers when you first encounter them: read the abstract and jump ahead to the results to roughly understand what they did. Decide later whether it's worth a deeper read.
Search tip: for, say, NLP involving Wikipedia, try expanding with NLP or site keywords; e.g., "wikipedia nlp," "wikipedia site:aclweb.org," "wikipedia ACL", etc.
Look at the ACL Anthology website: www.aclweb.org/anthology.

Suggested Papers

Here is a random sampling of papers that may be of interest, either as themselves or as jumping off points for others.

Brown et al. 1993. The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics.
Grosz et al. 1995. Centering: A Framework for Modeling the Local Coherence of Discourse. Computational Linguistics.
Pang et al., 2002. Thumbs Up? Sentiment Classification Using Machine Learning Techniques. Proceedings of EMNLP.
Collins, 2002. Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms. Proceedings of EMNLP.
Sauri and Pustejovsky, 2012. Are You Sure That This Happened? Assessing the Factuality Degree of Events in Text. Computational Linguistics.
Tsvetkov and Dyer, 2016. Cross-Lingual Bridges with Models of Lexical Borrowing. JAIR.
Eisenstein 2013. What to do about bad language on the internet. Proceedings of NAACL.
Dodge et al. 2019. Show Your Work: Improved Reporting of Experimental Results. Proceedings of EMNLP-IJCNLP.
Caliskan et al. 2017. Semantics derived automatically from language corpora contain human-like biases. Science.
Ramiro et al., 2018. Algorithms in the historical emergence of word senses. PNAS.

Possibly of interest: these ACL Anthology pages let you see number citations of papers for entire venues; you can rank by citation count (it only tracks within ACL Anthology papers) to see popular ones. They're not always interesting, but are sometimes.

Papers on text analysis as a tool for social science and the humanities:

Several papers from the Journal of Digital Humanities, 2(1). To start, see the overview: Weingar and Meeks, 2012. The Digital Humanities Contribution to Topic Modeling.
Grimmer and Stewart, 2013. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis.
Monroe et al., 2008. Fightin’ Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict. Political Analysis.
One of the social science-oriented papers from the webpage for Structural Topic Models.

Other areas.

Look at the list of Workshops on the ACL Anthology here. Workshops tend to have topically focused sets of papers, whose organizers and paper authors typically also have published in the area. All the other major conferences also have workshops.
Wikipedia can be hit-or-miss, but it isn't a terrible place to look. For example, Computational humor or Stylometry.