Analogical Frame Mapper (AFM) is a program for finding analogical relations in word embeddings.
It is the software associated with the paper: Unsupervised Mining of Analogical Frames by Constraint Satisfaction
De Vine, L., Geva, S., & Bruza, P. Unsupervised Mining of Analogical Frames by Constraint
Satisfaction. In Australasian Language Technology Association Workshop 2018 (p. 34).
An analogical frame is like a multi-dimensional proportional analogy.
Given a word embedding file in word2vec binary format, the program afmfind will search the word embeddings, attempting to discover analogical frames. The discovery process is specified by a number of command line arguments.
The code should compile with any C++11 conformant compiler. The code depends on the C++ linear algebra library Eigen, a copy of which is included in the source of this repository.
Example execution:
./afmfind -vecs my_vecs.bin -frames frames.txt -extends2 1 -extends3 1 -nns 15 -ext-nns 40
-p 0.3 -max-vecs 10000 -threads 8
Parameters:
frames the text file produced containing the frames.
max-vecs the maximum number of vectors (vocab) to include in the search.
threads the number of threads to search with.
p the value of the parallel constraint. Larger is more constrained, ie. fewer frames.
nns the number of vector neighbours to consider when assigning a value to an adjacent
variable in the frame.
ext-nns the number of neighbours to consider when assigning values to adjacent variables
when extending a frame.
Note that the effectiveness of increasing the number of threads depends on the values of other parameters. This is a result of how the computation is decomposed and assigned to worker tasks. Improvements in how work is decomposed will be included in future versions of the code.
Related Work
Hong, Y., & Lepage, Y. (2018). Production of Large Analogical Clusters from Smaller Example Seed Clusters Using Word Embeddings. In International Conference on Case-Based Reasoning (pp. 548-562). Springer, Cham.
Fam, R., & Lepage, Y. (2018). Tools for the production of analogical grids and a resource of n-gram analogical grids in 11 languages. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018).
Zhang, Y., Jatowt, A., & Tanaka, K. (2016, December). Towards understanding word embeddings: Automatically explaining similarity of terms. In 2016 IEEE International Conference on Big Data (Big Data) (pp. 823-832). IEEE.
Lepage, Y. (2014). Analogies between binary images: Application to chinese characters. In Computational Approaches to Analogical Reasoning: Current Trends (pp. 25-57). Springer, Berlin, Heidelberg.
Stroppa, N., & Yvon, F. (2005, June). An analogical learner for morphological analysis. In Proceedings of the Ninth Conference on Computational Natural Language Learning (pp. 120-127). Association for Computational Linguistics.