SRL using complete syntactic analysis Mihai Surdeanu and Jordi Turmo TALP Research Center Universitat Politècnica de Catalunya

Analysis of argument to constituent mappings One-to-one (shared parent)(different parents) Train 96.06% 2.49% 1.45% Dev 91.36% 4.83% 3.81% One-to-many

The approach One-to-one (shared parent)(different parents) Model learnedSimple heuristics One-to-many

One-to-one model: Features Argument structure features Argument context features Predicate structure features Predicate context features Distance features Most of them inspired from other works

Argument structure features: –syntactic label –head word, suffixes of 2, 3 and 4, lemma, and POS tag –content word computed as in [Surdeanu et al. 03], suffixes of 2, 3 and 4, lemma, and POS tag –first and last constituent words, POS tags –NE labels –binary features for temporal cue words (i.e., words that often occur in AM-TMP phrases in training) –number of occurrences of each possible syntactic label within the constituent –sequence of syntactic labels of the children constituents One-to-one model: Features

Argument context features : –phrase label, head word and POS tag of the constituent parent, and left and right immediate siblings Predicate structure features: –word and lemma –voice (active, passive, copulative, infinitive and progressive) –binary feature to indicate if the predicate is frequent (>2) Predicate context features: –sub-categorization rule: the rule that expands the predicate immediate parent One-to-one model: Features

Distance features : –syntactic path between argument and predicate (chain of labels) –length of the path –number of clauses in the path –number of verb phrases in the path –binary feature to indicate if the argument starts with a predicate particle –surface distance (set of features used in [Collins 99]) –generalized syntactic paths built using templates Arg ↑ Ancestor ↓ X ↓ Pred Arg ↑ X ↑ Ancestor ↓ Pred –subsumption count (difference between the depths in the tree of the predicate and the argument) One-to-one model: Features

One-to-one model:Classifiers Trained one-vs-all classifiers for the top 24 most common arguments Each classifier is developed using AdaBoost with confidence rated predictions [Shapire & Singer,99] Classifiers are combined using a greedy strategy: All predictions sorted in descending order of confidence Add prediction to the global solution if prediction does not violate the constraints related to the previously added predictions Arguments can not overlap No duplicates allowed for A0-A5 Only numeric arguments from PropBank frames are considered

One-to-many: Argument expansion heuristics 1.Argument mapped to one terminal phrase → extend its boundary to the right to include all terminal phrases sharing the same parent and not labeled for the same predicate 2.Argument preceeded/followed by quotes within the same constituent → extend its boundaries to include quotes

Results Trained with all positive examples and the first negative ones. PrecisionRecallF1 Dev.79.14%71.57%75.17% Test WSJ80.32%72.95%76.46% Test Brown72.41%59.67%65.42% Test WSJ+Brown79.35%71.17%75.04% Surpass with almost 6% the results of the best SRL that used partial syntax in the CoNLL’04 Only 0.14% out of the 75.17% in F are due to the argument expansion heuristics

Conclusions This is a simple approach –Basically one-to-one mappings –Greedy combination strategy Given the simplicity and the promising results obtained, it can be taken as a lower- limit for combination methods

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