TY - GEN
T1 - Grounding gradable adjectives through crowdsourcing
AU - Sharp, Rebecca
AU - Paul, Mithun
AU - Nagesh, Ajay
AU - Bell, Dane
AU - Surdeanu, Mihai
N1 - Publisher Copyright:
© LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.
PY - 2019
Y1 - 2019
N2 - In order to build technology that has the ability to answer questions relevant to national and global security, e.g., on food insecurity in certain parts of the world, one has to implement machine reading technology that extracts causal mechanisms from texts. Unfortunately, many of these texts describe these interactions using vague, high-level language. One particular example is the use of gradable adjectives, i.e., adjectives that can take a range of magnitudes such as small or slight. Here we propose a method for estimating specific concrete groundings for a set of such gradable adjectives. We use crowdsourcing to gather human language intuitions about the impact of each adjective, then fit a linear mixed effects model to this data. The resulting model is able to estimate the impact of novel instances of these adjectives found in text. We evaluate our model in terms of its ability to generalize to unseen data and find that it has a predictive R 2 of 0.632 in general, and 0.677 on a subset of high-frequency adjectives.
AB - In order to build technology that has the ability to answer questions relevant to national and global security, e.g., on food insecurity in certain parts of the world, one has to implement machine reading technology that extracts causal mechanisms from texts. Unfortunately, many of these texts describe these interactions using vague, high-level language. One particular example is the use of gradable adjectives, i.e., adjectives that can take a range of magnitudes such as small or slight. Here we propose a method for estimating specific concrete groundings for a set of such gradable adjectives. We use crowdsourcing to gather human language intuitions about the impact of each adjective, then fit a linear mixed effects model to this data. The resulting model is able to estimate the impact of novel instances of these adjectives found in text. We evaluate our model in terms of its ability to generalize to unseen data and find that it has a predictive R 2 of 0.632 in general, and 0.677 on a subset of high-frequency adjectives.
KW - Crowdsourcing
KW - Gradable adjectives
KW - Grounded semantics
UR - http://www.scopus.com/inward/record.url?scp=85059895968&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059895968&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85059895968
T3 - LREC 2018 - 11th International Conference on Language Resources and Evaluation
SP - 3348
EP - 3355
BT - LREC 2018 - 11th International Conference on Language Resources and Evaluation
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Piperidis, Stelios
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Hasida, Koiti
A2 - Mazo, Helene
A2 - Choukri, Khalid
A2 - Goggi, Sara
A2 - Mariani, Joseph
A2 - Moreno, Asuncion
A2 - Calzolari, Nicoletta
A2 - Odijk, Jan
A2 - Tokunaga, Takenobu
PB - European Language Resources Association (ELRA)
T2 - 11th International Conference on Language Resources and Evaluation, LREC 2018
Y2 - 7 May 2018 through 12 May 2018
ER -