Method: Implicit Tensor Factorization

Learning Embeddings for Transitive Verb Disambiguationby Implicit Tensor Factorization

Qualitative Evaluation: Nearest Neighbor Phrases & Multiple Meanings in Verb Matrices

Conclusions Adjuncts are very helpful in learning the meaning of transitive

verb phrases Tensor-based joint learning method is effective in capturing

the changes of the meaning of transitive verb phrases

Future Work Incorporating other informative contextual information, such as adjective-

noun relationships Applying our SVO embeddings to real-world NLP applications, such as

semantic search engines

Overview: Transitive Verb Sense Disambiguation using Embeddings and Matrices

CVSC 2015

Quantitative Evaluation: Results on Transitive Verb Sense Disambiguation Task

Kazuma Hashimoto and Yoshimasa Tsuruoka (University of Tokyo){hassy, tsuruoka} @logos.t.u-tokyo.ac.jp

A plausibility score for each tuple (p, 𝑎1, 𝑎2)An observed tuple Collapsed tuples

Jointly learning the composition function

Noun embeddings

Verb matrix

Cost function

Model parameters(randomly initialized)

Backpropagation

English Wikipedia corpus SVO: 23.6 million instances SVOPN: 17.3 million instances

Co-occurrence statistics of phrases!

Using Predicate-argument structures (Hashimoto et al., 2014)(Relationships between predicates and their arguments (phrases))

Extracting Subject-Verb-Object (SVO) tuples SVO-Preposition-Noun (SVOPN) tuples

Why prepositional adjuncts?Prepositional adjuncts complement the meaning of verb phrases!

Enju parser(Miyao et al., 2008)

Nearest neighbor verb-object phrases

make moneymake cash, make dollar, make profit,

earn baht, earn pound, earn billion

make paymentmake loan, make repayment, pay fine,

pay amount, pay surcharge, pay reimbursement

make use (of)use number, use concept, use approach,

use method, use model, use one

Biomedical domain

Computer science

Computer science

Management

Measuring semantic similarities between pairs of transitive verbs taking the same subjects and objects (Grefenstette et al., 2011)

Verb pair

with the same argumentsHuman rating

student write name

student spell name7

child show sign

child express sign6

system meet criterion

system visit criterion1

Evaluation:Spearman’s rank correlation between human ratings and the cosine similarity scores produced by the SVO embeddings

MethodSpearman’s rank

correlation score

This work (only SVO data) 0.480

This work (SVO and SVOPN data) 0.614

Tensor-based method (Milajevs et al., 2014) 0.456

Joint learning method (Hashimoto et al., 2014) 0.422

Adjuncts improve the score!

Finally, 50-dimensional embeddings are used