Main readings
The main readings for ANLP derive from two editions of the Jurafsky and Martin textbook:
- Second edition (denoted JM2)
- Third edition (denoted JM3)
These can be found on the ANLP Resource list. In order to view some resources on the list, you may need to be logged in with your EASE account or DICE account.
Accelerated Natural Language Processing Resource List
For more information on getting the most out of your courses Resource List, have a look at this video.
Optional readings
Linguistics background
In previous years some Informatics students have asked for more background reading on linguistics. A good place to start might be this text, which is also on the resource list, and available online through the University library:
Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax by Emily M Bender. Synthesis Lectures on Human Language Technologies, June 2013, Vol. 6, No. 3 , Pages 1-184.
Further mathematical details
Some students may want a more rigourous treatment of the models and machine learning methods we discuss. In that case I suggest the following textbook. It covers many of the same topics we do, but assumes somewhat more background and comfort with formal methods.
Introduction to Natural Language Processing by Jacob Eisenstein. MIT Press, 2019. (Draft version is available for free from author's github page here.)
Weekly optional readings
Other optional readings related to each week's topics are provided below for students who wish to learn more details, especially about recent research in the area. Some of these papers may also give you ideas for your IRR review. Many of the optional readings assume additional mathematical or machine learning background beyond what is covered in this course, but you may be able to understand the general idea of these papers by reading the introduction and skimming the rest, even if you cannot understand all of the details.
In the schedule below, we use the following key to optional readings:
- (A): These readings provide more detail about the week's topics, without requiring much additional knowledge of machine learning or later parts of this course.
- (B): The main concepts required to understand these readings are covered by the end of this course (though perhaps not all details).
- (M): These readings assume significant mathematical or machine learning background beyond what is covered in this course.
Week 1
- (A) Liu et al. (2023). We’re Afraid Language Models Aren’t Modeling Ambiguity. Evaluates modern large language models (LLM) on their ability to recognise and disentangle ambiguity, finding that they often fail to do so.
- (B) Schone and Jurafsky (2001). Knowledge-Free Induction of Inflectional Morphologies. Uses relatively simple methods to combine multiple sources of information, aiming to learn morphological relationships from a corpus without annotation.
- (B) Kirov et al. (2017). A Rich Morphological Tagger for English: Exploring the Cross-Linguistic Tradeoff Between Morphology and Syntax. Uses dependency syntax and neural models, which we will discuss later in this course.
- (B) Faruqui et al. (2016). Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning Uses a graph-based method to predict morpho-syntactic information for large lexicons from small seed lexicons, for a variety of languages.