February 2007CSA3050: Tagging III and Chunking 1 CSA2050: Natural Language Processing Tagging 3 and Chunking Transformation Based Tagging Chunking

February 2007CSA3050: Tagging III and Chunking 2 Tagging 3 and Chunking Lecture Slides based on Mike Rosner and Marti Hearst notes Additions from NLTK tutorials

February 2007CSA3050: Tagging III and Chunking 3 3 Approaches to Tagging 1.Rule-Based Tagger: ENGTWOL Tagger (Voutilainen 1995) 2.Stochastic Tagger: HMM-based Tagger 3.Transformation-Based Tagger: Brill Tagger (Brill 1995)

February 2007CSA3050: Tagging III and Chunking 4 Transformation-Based Tagging A combination of rule-based and stochastic tagging methodologies: –like rule-based tagging: rules are used to specify tags in a certain environment; –like stochastic tagging: machine learning is used. –Transformation-Based Learning (TBL)

February 2007CSA3050: Tagging III and Chunking 5 Transformation Based Error Driven Learning unannotated text initial state annotated text TRUTHlearner transformation rules diagram after Brill (1996)

February 2007CSA3050: Tagging III and Chunking 6 TBL Requirements Initial State Annotator List of allowable transformations Scoring function Search strategy

February 2007CSA3050: Tagging III and Chunking 7 Initial State Annotation Input –Corpus –Dictionary –Frequency counts for each entry Output –Corpus tagged with most frequent tags

February 2007CSA3050: Tagging III and Chunking 8 TBL Requirements Initial State Annotator List of allowable transformations Scoring function Search strategy

February 2007CSA3050: Tagging III and Chunking 9 Transformations Each transformation comprises A source tag A target tag A triggering environment Example NN VB Previous tag is TO

February 2007CSA3050: Tagging III and Chunking 10 More Examples Source tag Target Tag Triggering Environment NN VB previous tag is TO VBP VB one of the three previous tags is MD JJR RBR next tag is JJ VBP VB one of the two previous words is n’t

February 2007CSA3050: Tagging III and Chunking 11 Allowable transforms based on fixed schemas Schemat i-3 t i-2 t i-1 t i t i+1 t i+2 t i+3 1* 2* 3* 4* 5* 6* 7* 8* 9*

February 2007CSA3050: Tagging III and Chunking 12 Set of Possible Transformations The set of possible transformations is enumerated by allowing every possible tag or word in every possible slot in every possible schema This set can get quite large

February 2007CSA3050: Tagging III and Chunking 13 TBL Requirements Initial State Annotator List of allowable transformations Scoring function Search strategy

February 2007CSA3050: Tagging III and Chunking 14 Scoring Function For a given tagging state of the corpus For a given transformation For every word position in the corpus If the rule applies and yields a correct tag, increment score by 1 If the rule applies and yields an incorrect tag, decrement score by 1

February 2007CSA3050: Tagging III and Chunking 15 TBL Requirements Initial State Annotator List of allowable transformations Scoring function Search strategy

February 2007CSA3050: Tagging III and Chunking 16 The Basic Algorithm Label every word with its most likely tag Repeat the following while improvement > threshold –Examine every possible transformation, selecting the one that results in the most improved tagging –Retag the data according to this rule –Append this rule to output list Return output list of transformations

February 2007CSA3050: Tagging III and Chunking 17 TBL: Remarks Execution Speed: TBL tagger is slower than HMM approach. Learning Speed is slow: Brill’s implementation over a day (600k tokens) BUT … Learns small number of simple, non- stochastic rules Can be made to work faster with Finite State Transducers

February 2007CSA3050: Tagging III and Chunking 18 Tagging Unknown Words New words added to (newspaper) language 20+ per month Plus many proper names … Increases error rates by 1-2% Methods Assume the unknowns are nouns. Assume the unknowns have a probability distribution similar to words occurring once in the training set. Use morphological information, e.g. words ending with –ed tend to be tagged VBN.

February 2007CSA3050: Tagging III and Chunking 19 Evaluation The result is compared with a manually coded “Gold Standard” –Typically accuracy reaches 95-97% –This may be compared with the result for a baseline tagger (one that uses no context). Important: 100% accuracy is impossible even for human annotators.

February 2007CSA3050: Tagging III and Chunking 20 A word of caution 95% accuracy: every 20th token wrong 96% accuracy: every 25th token wrong –an improvement of 25% from 95% to 96% ??? 97% accuracy: every 33th token wrong 98% accuracy: every 50th token wrong

February 2007CSA3050: Tagging III and Chunking 21 How much training data is needed? When working with the STTS (50 tags) we observed a strong increase in accuracy when testing on 10´000, 20´000, …, 50´000 tokens, a slight increase in accuracy when testing on up to 100´000 tokens, hardly any increase thereafter.

February 2007CSA3050: Tagging III and Chunking 22 Summary Tagging decisions are conditioned on a wider range of events that HMM models mentioned earlier. For example, left and right context can be used simultaneously. Learning and tagging are simple, intuitive and understandable. Transformation-based learning has also been applied to sentence parsing.

February 2007CSA3050: Tagging III and Chunking 23 The Three Approaches Compared Rule Based –Hand crafted rules –It takes too long to come up with good rules –Portability problems Stochastic –Find sequence with highest probability (Viterbi) –Result of training not accessible to humans –Large storage needs for intermediate results whilst training Transformation –Rules are learned –Small number of rules –Rules can be inspected and modified by humans

February 2007CSA3050: Tagging III and Chunking 24 Shallow/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

February 2007CSA3050: Tagging III and Chunking 25 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]. Question answering: –What [Spanish explorer] discovered [the Mississippi River]?

February 2007CSA3050: Tagging III and Chunking 26 Motivation Locating information –e.g., text retrieval Index a document collection on its noun phrases Ignoring information –Generalize in order to study higher-level patterns e.g. phrases involving “gave” in Penn treebank: –gave NP; gave up NP in NP; gave NP up; gave NP help; gave NP to NP –Sometimes a full parse has too much structure Too nested Chunks usually are not recursive

February 2007CSA3050: Tagging III and Chunking 27 Representation BIO (or IOB) Trees

February 2007CSA3050: Tagging III and Chunking 28 Comparison with Full Parsing Parsing is usually an intermediate stage –Builds structures that are used by later stages of processing Full parsing is a sufficient but not necessary intermediate stage for many NLP tasks –Parsing often provides more information than we need Shallow parsing is an easier problem –Less word-order flexibility within chunks than between chunks –More locality: Fewer long-range dependencies Less context-dependence Less ambiguity

February 2007CSA3050: Tagging III and Chunking 29 Chunks and Constituency Constituents: [[a tall man] [ in [the park]]]. Chunks: [a tall man] in [the park]. A constituent is part of some higher unit in the hierarchical syntactic parse Chunks are not constituents – Constituents are recursive But, chunks are typically subsequences of constituents – Chunks do not cross major constituent boundaries

February 2007CSA3050: Tagging III and Chunking 30 Chunk Parsing in NLTK Chunk parsers usually ignore lexical content –Only need to look at part-of-speech tags Possible steps in chunk parsing –Chunking, unchunking –Chinking –Merging, splitting Evaluation –Compare to a Baseline –Evaluate in terms of Precision, Recall, F-Measure Missed (False Negative), Incorrect (False Positive)

February 2007CSA3050: Tagging III and Chunking 31 Chunk Parsing in NLTK Define a regular expression that matches the sequences of tags in a chunk A simple noun phrase chunk regexp: (Note that matches any tag starting with NN) ? * 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, first 1 gets priority

February 2007CSA3050: Tagging III and Chunking 32 Unchunking Remove any chunk with a given pattern –e.g., unChunkRule(‘ +’, ‘Unchunk NNDT’) –Combine with Chunk Rule + Chunk all matching subsequences: –Input: the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN –Apply chunk rule [ the/DT little/JJ cat/NN ] sat/VBD on/IN [ the/DT mat/NN ] –Apply unchunk rule [ the/DT little/JJ cat/NN ] sat/VBD on/IN the/DT mat/NN

February 2007CSA3050: Tagging III and Chunking 33 Chinking A chink is a subsequence of the text that is not a chunk. Define a regular expression that matches the sequences of tags in a chink A simple chink regexp for finding NP chunks: ( | )+ First apply chunk rule to chunk everything –Input: the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN –ChunkRule(' +', ‘Chunk everything’) [ the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN ] –Apply Chink rule above: [ the/DT little/JJ cat/NN ] sat/VBD on/IN [ the/DT mat/NN ]

February 2007CSA3050: Tagging III and Chunking 34 Merging Combine adjacent chunks into a single chunk –Define a regular expression that matches the sequences of tags on both sides of the point to be merged Example: –Merge a chunk ending in JJ with a chunk starting with NN MergeRule(‘ ’, ‘ ’, ‘Merge adjs and nouns’) [ 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 Splitting is the opposite of merging

February 2007CSA3050: Tagging III and Chunking 35 Merging Combine adjacent chunks into a single chunk –Define a regular expression that matches the sequences of tags on both sides of the point to be merged Example: –Merge a chunk ending in JJ with a chunk starting with NN MergeRule(‘ ’, ‘ ’, ‘Merge adjs and nouns’) [ 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 Splitting is the opposite of merging

February 2007CSA3050: Tagging III and Chunking 36 Next Sessions… NLTK Exercises