1. Chunk Parsing

2. 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: 1.Tokenize 2.Tag 3.Chunk

4. Chunk Parsing Like tokenizing and tagging in a few respects: 1.Can skip over material in the input 2.Often finite-state (or finite-state like) methods are used (applied over tags) 3.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-np'at'uhan-ebats-ets child-NOMwindow-ACCopen-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-np'at'uhan-ebats-ets child-NOMwindow-ACCopen-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-np'at'uhan-ebats-ets child-NOMwindow-ACCopen-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??) DT NN VBP DT NN

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

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-np'at'uhan-ebats-ets child-NOMwindow-ACCopen-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-np'at'uhan-ebats-ets child-NOMwindow-ACCopen-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-np'at'uhan-ebats-ets child-NOMwindow-ACCopen-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-np'at'uhan-ebats-ets child-NOMwindow-ACCopen-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??) DT NN VBP DT NN NP VP

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-np'at'uhan-ebats-ets child-NOMwindow-ACCopen-AOR.3SG ‘The child opened the window.’ (Megerdoomian ??) DT NN VBP DT NN NP VP

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