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School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Chunking: Shallow Parsing Eric Atwell, Language Research Group.

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Presentation on theme: "School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Chunking: Shallow Parsing Eric Atwell, Language Research Group."— Presentation transcript:

1 School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Chunking: Shallow Parsing Eric Atwell, Language Research Group

2 Shallow Parsing Break text up into non-overlapping contiguous subsets of tokens. Also called chunking, partial parsing, light parsing. What is it useful for? – semantic patterns Finding key meaning-elements: Named Entity Recognition people, locations, organizations Studying linguistic patterns, e.g. semantic patterns of verbs gave NP gave up NP in NP gave NP NP gave NP to NP Can ignore complex structure when not relevant

3 A Relationship between Segmenting and Labeling Tokenization segments the text Tagging labels the text Shallow parsing does both simultaneously.

4 Chunking vs. Full Syntactic Parsing G.K. Chesterton, author of The Man who was Thursday

5 Representations for Chunks IOB tags Inside, outside, and begin In English, the start of a phrase is often marked by a function-word

6 Representations for Chunks Trees Chunk structure is a two-level tree that spans the entire text, containing both chunks and non-chunks

7 CONLL Corpus: training data for Machine Learning of chunking From the Conference on Natural Language Learning Competition from 2000 Goal: create machine learning methods to improve on the chunking task

8 CONLL Corpus Data in IOB format from WSJ Wall Street Journal: Word POS-tag IOB-tag Training set: 8936 sentences Test set: 2012 sentences Tags from the Brill tagger Penn Treebank Tags Evaluation measure: F-score 2*precision*recall / (recall+precision) Baseline was: select the chunk tag that is most frequently associated with the POS tag, F =77.07 Best score in the contest was F=94.13

9 Chunking with Regular Expressions This time we write regexs over TAGS rather than characters ? + Compile them with parse.ChunkRule() rule = parse.ChunkRule( +) chunkparser = parse.RegexpChunk([rule], chunk_node = NP) Resulting object is a (sort-of) parse tree Top-level node called S Chunks are labelled NP

10 Chunking with Regular Expressions

11 Rule application is sensitive to order

12 Chinking Specify what does not go into a chunk. Kind of like specifying punctuation as being not alphanumeric and spaces. Can be more difficult to think about.

13 Simple chink-chunk approach: function v content word-class Regular expressions for chunks and chinks CAN get complex BUT the whole point is to be simpler than full parsing! SO: use a simple model which works reasonably well (then tidy up afterwards…) Chunk = nominal content-word (noun) Chink = others (verb, pronoun, determiner, preposition, conjunction) (+adjective, adverb as a borderline category)

14 Example Fruit flies like a banana fruit\N flies\N like\V a\A banana\N [fruit flies] like a [banana] [S [NP fruit\N flies\N NP] [VP like\V [NP a\A banana\N NP] VP] S]

15 An alternative parse This sentence is grammatically ambiguous: Fruit flies like a banana fruit\N flies\N like\V a\A banana\N [fruit flies] like a [banana] fruit\N flies\V like\I a\A banana\N [fruit] flies like a [banana] cf: bank robbers like a chase v bread bakes in an oven [S [NP fruit\N NP] [VP flies\V [PP like\I [NP a\A banana\N NP] PP] VP] S]

16 Ambiguity leads to more rules fruit\N flies\N like\V a\A banana\N [fruit flies] like a [banana] fruit\N flies\V like\I a\A banana\N [fruit] flies like a [banana] BUT what about: Time flies like an arrow - time\N, time\V time\N flies\N like\V an\A arrow\N [time flies] like an [arrow] time\N flies\V like\I an\A arrow\N [time] flies like an [arrow] time\V flies\N like\I an\A arrow\N time [flies] like an [arrow] 3 rd PoS-tagging gives ambiguous parse

17 Chunking can predict prosodic breaks An Approach for Detecting Prosodic Phrase Boundaries in Spoken English An Approach for Detecting Prosodic Phrase Boundaries in Spoken English by Claire Brierley and Eric AtwellClaire BrierleyEric Atwell

18 Summary Shallow parsing is useful for: Entity recognition people, locations, organizations Studying linguistic patterns gave NP gave up NP in NP gave NP NP gave NP to NP Prosodic phrase breaks – pauses in speech Can ignore complex structure when not relevant Chink-chunk approach: quick-and-dirty chunking, content v function PoS Chink-chunk parsing is simpler than context-free grammar parsing!


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