Unsupervised Acquisition of Axioms to Paraphrase Noun Compounds and Genitives CICLING 2012, New Delhi Anselmo Peñas NLP & IR Group, UNED, Spain Ekaterina Ovchinnikova USC – Information Science Institute, USA

UNED nlp.uned.es Texts omit information Humans optimize language generation effort We omit information that we know the receptor is able to predict and recover Our research goal is to make explicit the omitted information in texts

UNED nlp.uned.es Implicit predicates In particular, some noun compounds and genitives are used in such way In these cases, we want to recover the implicit predicates For example: Morning coffee -> coffee drunk in the morning Malaria mosquito -> mosquito that carries malaria

UNED nlp.uned.es How to find the candidates? Nakov & Hearst 2006 Search the web N1 N2 -> N2 THAT * N1 Malaria mosquito -> mosquito THAT * malaria Here we use Proposition Stores Harvest a text collection that will serve as context Parse documents Count N-V-N, N-V-P-N, N-P-N, … structures Build Proposition Stores (Peñas & Hovy, 2010)

UNED nlp.uned.es Proposition Stores Example: propositions that relate Bomb, attack npn:[bomb:n, in:in, attack:n]:13. nvpn:[bomb:n, explode:v, in:in, attack:n]:11. nvnpn:[bomb:n, kill:v, people:n, in:in, attack:n]:8. npn:[attack:n, with:in, bomb:n]:8. … All of them could be paraphrases for the noun compound “bomb attack”

UNED nlp.uned.es NE Semantic Classes Now, What happens if we have a Named Entity? Shakespeare’s tragedy -> write Why? Consider John’s tragedy Airbus’ tragedy

UNED nlp.uned.es NE Semantic Classes We are considering the “semantic classes” of the NE Shakespeare -> writer writer, tragedy -> write

UNED nlp.uned.es Class-Instance relations Fortunately, relevant semantic classes are pointed out in texts through well-known structures appositions, copulative verbs, “such as”, … Here we take advantage of dependency parsing to get class-instance relations NNP NN nn NNP NN appos NNP NN be

UNED nlp.uned.es Class-Instance relations World News has_instance(leader,'Yasir':'Arafat'):1491. has_instance(spokesman,'Marlin':'Fitzwater'):1001. has_instance(leader,'Mikhail':'S.':'Gorbachev'):980. has_instance(chairman,'Yasir':'Arafat'):756. has_instance(agency,'Tass'):637. has_instance(leader,'Radovan':'Karadzic'):611. has_instance(adviser,'Condoleezza':'Rice'):590. …

UNED nlp.uned.es So far Propositions: | P(p,a) p: predicate a: list of arguments P(p,a): joint probability Class-instance relations: | P(c,i) c: class i: instance P(c,i): joint probability

UNED nlp.uned.es Probability of a predicate Let’s consider the following example Favre pass Assume the text has pointed out he is a quarterback What is Favre doing with the pass? The same as other quarterbacks The quarterbacks we observed before in the background collection – Proposition Store

UNED nlp.uned.es Probability of a predicate Favre pass -> p | P(p|i) Favre -> quarterback | P(c|i) quarterback, pass -> throw | P(p|c) We already have: We need to estimate: P(p|c) (What other quarterbacks do with passes)

UNED nlp.uned.es Probability of a predicate quarterback pass -> p | P(p|c) Steve:Young pass -> throw | P(p|i) Culpepper pass -> complete | P(p|i) … We already have and P(p|i) comes from previous observation: Proposition Store

UNED nlp.uned.es Evaluation We want to address the following questions Do we find the paraphrases required to enable Textual Entailment? Do all the noun-noun dependencies need to be paraphrased? How frequently NEs appear in them?

UNED nlp.uned.es Experimental setting Proposition Store from 216,303 World News 7,800,000 sentences parsed RTE-2 (Recognizing Textual Entailment) 83 entailment decisions depend on noun-noun paraphrases 77 different noun-noun paraphrases

UNED nlp.uned.es Results How frequently NEs appear in these pairs? 82% of paraphrases contain at least one NE 62% are paraphrasing NE-N (e.g. Vikings quarterback)

UNED nlp.uned.es Results Do all the noun-noun dependencies need to be paraphrased? No, only 54% in our test set Some compounds encode semantic relations such as: 12% are locative relations (e.g. New York club) Temporal relations (e.g. April 23 rd strike, Friday semi-final) Class-instance relations (e.g. quarterback Favre) Measure, … Some are trivial: 27% are paraphrased with “of”

UNED nlp.uned.es Results Do we find the paraphrases required to enable Textual Entailment? Yes in 63% of non-trivial cases Proposition type Paraphrase NPNJackson trial ↔ trial against Jackson engine problem ↔ problem with engine NVNU.S. Ambassador ↔ Ambassador represents the U.S. ETA bombing ↔ ETA carried_out bombing NVNPNwife of Joseph Wilson ↔ wife is married to Joseph Wilson NVPNVietnam veteran ↔ veteran comes from Vietnam Shapiro’s office ↔ Shapiro work in office Germany's people ↔ people live in Germany Abu Musab al-Zarqawi's group ↔ group led by Abu Musab al-Zarqawi

UNED nlp.uned.es Results RTE-2 pair 485: Paraphrase not found United Nations vehicle ↔ United Nations produces vehicles United Nations doesn’t share any class with the instances that “produce vehicles” Toyota vehicle -> develop, build, sell, produce, make, export, recall, assemble, …

UNED nlp.uned.es Conclusions A significant proportion of noun-noun dependencies includes Named Entities Some noun-noun dependencies don’t require the retrieval of implicit predicates The method proposed is sensitive to different Nes Different NEs retrieve different predicates Current work: to select the most relevant paraphrase according to the text We are exploring weighted abduction

Unsupervised Acquisition of Axioms to Paraphrase Noun Compounds and Genitives CICLING 2012, New Delhi Thanks!