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Chunk Parsing

Chunk Parsing

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Chunk Parsing

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  1. Chunk Parsing

  2. Chunk Parsing • Also called chunking, light parsing, or partial parsing. • Method: Assign some additional structure to input over tagging • Used when full parsing not feasible or not desirable. • Because of the expense of full-parsing, often treated as a stop-gap solution.

  3. Chunk Parsing • No rich hierarchy, as in parsing. • Usually one layer above tagging. • The process: • Tokenize • Tag • Chunk

  4. Chunk Parsing • Like tokenizing and tagging in a few respects: • Can skip over material in the input • Often finite-state (or finite-state like) methods are used (applied over tags) • Often application specific (i.e., the chunks tagged have uses for particular applications)

  5. Chunk Parsing • Chief Motivations: to find data or to ignore data • Example from Bird and Loper: find the argument structures for the verb give. • Can “discover” significant grammatical structures before developing a grammar: gave NP gave up NP in NP gave NP up gave NP help gave NP to NP

  6. Chunk Parsing • Like parsing, except: • It is not exhaustive, and doesn’t pretend to be. • Structures and data can be skipped when not convenient or not desired • Structures of fixed depth produced • Nested structures typical in parsing [S[NP The cow [PP in [NP the barn]]] ate • Not in chunking [NP The cow] in [NP the barn] ate

  7. Chunk Parsing • Finds contiguous, non-overlapping spans of related text, and groups them into chunks. • Because contiguity is given, finite state methods can be adapted to chunking

  8. Longest Match • Abney 1995 discusses longest match heuristic: • One automaton for each phrasal category • Start automata at position i (where i=0 initially) • Winner is the automaton with the longest match

  9. Longest Match • He took chunks from the PTB: NP → D N NP → D Adj N VP → V • Encoded each rule as an automaton • Stored longest matching pattern (the winner) • If no match for a given word, skipped it (in other words, didn’t chunk it) • Results: Precision .92, Recall .88

  10. An Application • Data-Driven Linguistics Ontology Development (NSF BCE-0411348) • One focus: locate linguistically annotated (read: tagged) text and extract linguistically relevant terms from text • Attempt to discover “meaning” of the terms • Intended to build out content of the ontology (GOLD) • Focus on Interlinear Glossed Text (IGT)

  11. An Application • Interlinear Glossed Text (IGT), some examples: (1)Afisi a-na-ph-a nsomba hyenas SP-PST-kill-ASP fish `The hyenas killed the fish.' (Baker 1988:254)

  12. An Application • More examples: (4) a. yerexa-n p'at'uhan-e bats-ets child-NOM window-ACC open-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??)

  13. An Application • Problem: How do we ‘discover’ the meaning of the linguistically salient terms, such as NOM, ACC, AOR, 3SG? • Perhaps we can discover the meanings by examining the contexts in which the occur. • POS can be a context. • Problem: POS tags rarely used in IGT • How do you assign POS tags to a language you know nothing about? • IGT gives us aligned text for free!! (4) a. yerexa-n p'at'uhan-e bats-ets child-NOM window-ACC open-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??)

  14. An Application • IGT gives us aligned text for free!! • POS tag the English translation • Align with the glosses and language data • That helps. We now know that NOM and ACC attach to nouns, not verbs (nominal inflections) • And AOR and 3SG attach to verbs (verbal inflections) (4) a. yerexa-n p'at'uhan-e bats-ets child-NOM window-ACC open-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??) DT NN VBP DT NN

  15. An Application • In the LaPolla example, we know that NOM does not attach to nouns, but to verbs. Must be some other kind of NOM. (4) a. yerexa-n p'at'uhan-e bats-ets child-NOM window-ACC open-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??) DT NN VBP DT NN

  16. An Application • How we tagged: • Globally applied most frequent tags (stupid tagger) • Repaired tags where context dictated a change (e.g., TO preceding race = VB) • Technique similar to Brill 1995 (4) a. yerexa-n p'at'uhan-e bats-ets child-NOM window-ACC open-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??) DT NN VBP DT NN

  17. An Application • But can we get more information about NOM, ACC, etc.? • Can chunking tell us something more about these terms? • Yes! (4) a. yerexa-n p'at'uhan-e bats-ets child-NOM window-ACC open-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??) DT NN VBP DT NN

  18. An Application • Chunk phrases, mainly NPs • Since relationship (in simple sentences) between NPs and verbs tells us something about the verbs’ arguments (Bird and Loper 2005)… • We can tap this information to discover more about the linguistic tags (4) a. yerexa-n p'at'uhan-e bats-ets child-NOM window-ACC open-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??) DT NN VBP DT NN

  19. An Application • Apply Abney 1995’s longest match heuristic to get as many chunks as possible (especially NP) • Leverage English canonical SVO (NVN) order to identify simple argument structures • Use these to discover more information about the terms • Thus… (4) a. yerexa-n p'at'uhan-e bats-ets child-NOM window-ACC open-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??) DT NN VBP DT NN NP VP NP

  20. An Application • We know that • NOM attaches to subject NPs – may be a case marker indicating subject • ACC attaches to object NPs – may be a case marker indicating object (4) a. yerexa-n p'at'uhan-e bats-ets child-NOM window-ACC open-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??) DT NN VBP DT NN NP VP NP

  21. An Application • What we do next: look at co-occurrence relations (clustering) of • Terms with terms • Host categories with terms • To determine more information about the terms • Done by building feature vectors of the various linguistic grammatical terms (“grams”) representing their contexts • And measuring relative distances between these vectors (in particular, for terms we know)

  22. Linguistic “Gram” Space