Stanford CS224N - NLP w/ DL | Winter 2021 | Lecture 4 - Syntactic Structure and Dependency Parsing

[@TheRohit901]

39:20, how is the Prof so calm and neutral while explaining this lmao, I would've bursted out laughing

[@rakshithpoojary1027]

🤣🤣

[@qingqiqiu]

As a Chinese, I've got learnt lots of grammar from this lecture ...

[@yuhangliu7451]

哈哈，我也是被语法搞懵逼了

[@chenmarkson7413]

这些还好吧 大家没有被要求背过语法吗hhh

[@AramR-m2w]

🎯 Key Takeaways for quick navigation: 00:05 Today's *lecture focuses on syntactic structure, introducing ideas of constituency and dependency, leading to dependency parsing in natural language processing.* 01:35 The *lecture covers transition-based dependency parsing and the development of a simple yet highly effective neural dependency parser, which is the focus of the third assignment.* 03:02 A *reminder about PyTorch installation for Assignment 2 and an upcoming PyTorch tutorial is provided. Students are encouraged to start thinking about final projects.* 03:28 The *lecture delves into how syntactic structure contributes to conveying meaning in human language, emphasizing the role of part-of-speech tags and constituency grammars.* 15:31 The *shift towards dependency grammars, particularly universal dependencies, is explained. The lecture introduces the concept of dependency structure, exemplifying with arrows representing dependencies between words in a sentence.* 20:54 The *importance of understanding syntactic structure is highlighted for both human language interpretation and building effective neural network models in natural language processing.* 23:46 Prepositional *phrase attachment ambiguities in sentences are illustrated with real-world examples, emphasizing the role of context in resolving such ambiguities.* 28:50 Complex *sentences with multiple prepositional phrases are presented to illustrate the challenges in determining the correct attachment of phrases within a sentence.* 30:18 Human *language sentences are highly ambiguous due to the chaining of prepositional phrases, resulting in an exponential number of parses based on Catalan numbers.* 34:39 Coordination *and scope ambiguities in sentences can lead to different interpretations, as seen in examples where the meaning changes based on the grouping of phrases.* 37:01 Ambiguities *can arise from headlines due to the omission of conjunctions, leading to different readings of the intended message.* 38:01 Syntactic *ambiguity in sentences with adjectives and adverbs, such as "Students get first hand job experience," demonstrates how modifiers can alter interpretations.* 40:02 Verb *phrases can introduce attachment ambiguities, as shown in the headline "Mutilated body washes up on Rio beach to be used for Olympics beach volleyball," where the interpretation depends on the attachment of the verb phrase.* 43:48 Dependency *grammars involve relations between pairs of words, with arrows indicating dependencies. The syntactic structure forms a tree with a single root, and dependencies are annotated in a treebank created by human annotators.* 51:20 Treebanks, *created by hand-parsing sentences, have become crucial for training and evaluating natural language processing systems, providing reusable data for various linguistic studies and psycholinguistic models.* 57:18 Parsing *relies on information from words, distance, intervening material, punctuation, and valency to determine dependencies and build syntactic structures for sentences.* 01:00:31 Dependency *parsing involves determining the syntactic structure of a sentence, with words forming a tree structure based on dependencies.* 01:01:29 Crossing *dependencies can occur when phrases are split in non-projective sentences, deviating from the linear order of words.* 01:02:56 Dependency *parses corresponding to context-free grammar trees must be projective, but dependency grammars allow non-projective structures for displaced constituents.* 01:03:25 Preposition *stranding in English, like "who did Bill buy the coffee from yesterday," leads to non-projective dependency structures.* 01:04:50 Dependency *parsers can be built using dynamic programming, graph algorithms, or transition-based parsing. Transition-based parsing, like shift-reduce parsing, involves a set of transitions guided by a classifier, allowing linear time parsing.* 01:06:47 Greedy *transition-based parsers, popularized by Joakim Nivre, predict the next parsing action based on machine learning classifiers, achieving linear parsing time.* 01:13:33 Symbolic *features of stack and buffer configurations are used to train classifiers in transition-based dependency parsing.* 01:19:33 Evaluation *metrics for dependency parsers include unlabeled accuracy (matching dependencies) and labeled accuracy (matching dependencies with labels).* Made with HARPA AI

[@nanunsaram]

1:04:30 Methods of Dependency Parsing 1:06:20 Dependency parsing with example (Nivre 2003)

[@vedgupta1686]

Hey prof, the examples are hilarious! XD

[@josephchan5071]

18:15-19:25 Prepositions are dependent on case-marked nouns (in Universal Dependencies)

[@drjenschn]

Technically, factorials should even grow super-exponentially, roughly to the tune of exp(n log n) which should be equivalent to n^n. Catalan numbers are of this kind...

[@SPARKIE-l3g]

hey , please some help me out . i am getting difficulty in making relation in words , suggest me some way to improve

[@MenTaLLyMenTaL]

@53:00 What does handwriting a grammar mean?

[@charlieji8781]

I think that it refers to labeling parsers to words in a particular sentence, i.e. the sad cat as a noun phrase.

[@robertorrw2]

He handwrites a grammar at 7:19, then at 11:39 he ends with "I can keep on doing things of this sort", and by that he means handwriting a grammar. You can look up "Context-free grammar" to learn more on this, and to make sense of what it means to "write a grammar".

[@keonlo123]

Here I think "grammar" means the particular rules of syntax in a particular grammar.

[@PrathamMittal-t8m]

writing a grammar is a whole other topic he just skimmed throught for cfg there are rules to write a cfg you should read about it somewhere or watch some youtube videos , its also covered in theory of computation course in some uni

[@varungp]

ME: The news headline is "San Jose cops kill man with knife." What is the meaning of this headline? CHATGPT: The meaning of the headline "San Jose cops kill man with knife" is that the police officers in San Jose, California used lethal force to stop a man who was armed with a knife. The headline suggests that the man was killed as a result of the interaction with the police.

[@PrathamMittal-t8m]

Does this play list covers the coding aspect even a little?

[@paulhowrang]

same content from years, some of these lectures have less relevance now, this course needs massive revamping, there is even a newer version of this course but content is more or less the same ..

[@drjenschn]

Not a English native speaker, but shouldn't that be "Whom did Bill buy the coffee from yesterday?" That would also eliminate some ambiguity (not that spoken English offers such kindness...)

[@shubhankar3164]

38:25 NO WAY

[@saiprasaths2595]

Why didn't he explain it? 🤣🤣

[@shalinkaji3024]

🤣

[@AmbitionAlign]

"um"

[@UKIsNotCuisineDesert]

somehow I'm thinking about Master Yoda.

[@goodwilrv]

As an Indian, I feel pride to see ancient India's PANINI's mention as the first linguistic in the human history, 500 years before Christ Indians were researching the dependency structure.

[@陈旭缘]

他是怎么忍住不笑的？

[@user-mp1br6yh1g]

We should train a model to write comical double entendre meanings. Then AI could take over from todays crap standup comedians. Its' a million dollar idea!