Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books עיבוד שפות טבעיות - שיעור שבע Partial Parsing אורן גליקמן המחלקה למדעי המחשב אוניברסיטת בר אילן

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Syntax The study of grammatical relations between words and other units within the sentence. The Concise Oxford Dictionary of Linguistics the way in which linguistic elements (as words) are put together to form constituents (as phrases or clauses) Merriam-Webster Dictionary

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Brackets “I prefer a morning flight” [ S [ NP [ pro I]][ VP [ V prefer][ NP [ Det a] [ Nom [ N morning] [ N flight]]]]]]

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Parse Tree Noun Nom NounDet VerbPronoun Ipreferamorning flight NP S VP

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Parsing The problem of mapping from a string of words to to its parse tree is called parsing.

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Generative Grammar A set of rules which indicate precisely what can be and cannot be a sentence in a language. A grammar which precisely specifies the membership of the set of all the grammatical sentences in the language in question and therefore excludes all the ungrammatical sentences.

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Formal Languages The set of all grammatical sentences in a given natural language. Are natural languages regular?

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books English is not a regular language! a n b n is not regular Look at the following English sentences: –John and Mary like to eat and sleep, respectively. –John, Mary, and Sue like to eat, sleep, and dance, respectively. –John, Mary, Sue, and Bob like to eat, sleep, dance, and cook, respectively.

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Constituents Certain groupings of words behave as constituents. Constituents are able to occur in various sentence positions: –ראיתי את הילד הרזה –ראיתי אותו מדבר עם הילד הרזה –הילד הרזה גר ממול

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books The Noun Phrase (NP) Examples: –He –Ariel Sharon –The prime minister –The minister of defense during the war in Lebanon. They can all appear in a similar context: ___ was born in Kfar-Malal

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Prepositional Phrases Examples: –the man in the white suit –Come and look at my paintings –Are you fond of animals? –Put that thing on the floor

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Verb Phrases Examples: –Getting to school on time was a struggle. –He was trying to keep his temper. –That woman quickly showed me the way to hide.

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Chunking Text chunking is dividing sentences into non- overlapping phrases. Noun phrase chunking deals with extracting the noun phrases from a sentence. While NP chunking is much simpler than parsing, it is still a challenging task to build a accurate and very efficient NP chunker.

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books What is it good for The importance of chunking derives from the fact that it is used in many applications: –Information Retrieval & Question Answering –Machine Translation –Preprocessing before full syntactic analysis –Text to speech –Many other Applications

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books What kind of structures should a partial parser identify? Different structures useful for different tasks: –Partial constituent structure [ NP I] [ VP saw [ NP a tall man in the park]]. –Prosodic segments [I saw] [a tall man] [in the park]. –Content word groups [I] [saw] [a tall man] [in the park].

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Chunk Parsing Goal: divide a sentence into a sequence of chunks. Chunks are non-overlapping regions of a text: –[I] saw [a tall man] in [the park]. Chunks are non-recursive –a chunk can not contain other chunks Chunks are non-exhaustive –not all words are included in chunks

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Chunk Parsing Examples Noun-phrase chunking: –[I] saw [a tall man] in [the park]. Verb-phrase chunking: –The man who [was in the park] [saw me]. Prosodic chunking: –[I saw] [a tall man] [in the park].

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Chunks and Constituency Constituents: [a tall man in [the park]]. Chunks: [a tall man] in [the park]. Chunks are not constituents –Constituents are recursive Chunks are typically subsequences of Constituents –Chunks do not cross constituent boundaries

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Chunk Parsing: Accuracy Chunk parsing achieves higher accuracy –Smaller solution space –Less word-order flexibility within chunks than between chunks –Better locality: Fewer long-range dependencies Less context dependence –No need to resolve ambiguity –Less error propagation

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Chunk Parsing: Domain Specificity Chunk parsing is less domain specific: Dependencies on lexical/semantic information tend to occur at levels "higher" than chunks: –Attachment –Argument selection –Movement Fewer stylistic differences within chunks

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Chunk Parsing: Efficiency Chunk parsing is more efficient –Smaller solution space –Relevant context is small and local –Chunks are non-recursive –Chunk parsing can be implemented with a finite state machine

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Psycholinguistic Motivations Chunk parsing is psycholinguistically motivated: Chunks as processing units –Humans tend to read texts one chunk at a time – Eye-movement tracking studies Chunks are phonologically marked –Pauses, Stress patterns Chunking might be a first step in full parsing

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Chunk Parsing Techniques Chunk parsers usually ignore lexical content Only need to look at part-of-speech tags Techniques for implementing chunk parsing: –Regular expression matching / Finite State Machines –Transformation Based Learning –Memory Based Learning –Others

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Regular Expression Matching Define a regular expression that matches the sequences of tags in a chunk –A simple noun phrase chunk regexp: ? * –Chunk all matching subsequences: the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN [the/DT little/JJ cat/NN] sat/VBD on/IN [the/DT mat/NN] –If matching subsequences overlap, the first one gets priority

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Chunking as Tagging Map Part of Speech tag sequences to {I,O,B}*  I – tag is part of an NP chunk  O – tag is not part of  B – the first tag of an NP chunk which immediately follows another NP chunk Example: –Input: The little cat sat on the mat –Output: B I I O O B I

Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Chunking State of the Art Depending on task specification and test set: 90-95%

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Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books Context Free Grammars Putting the constituents together Next Week…