Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem (NAACL 2022)

We proposed one-dimensional word embeddings.

Paper: https://arxiv.org/abs/2205.01954

💡 How to Use

wordtour.txt is the trained embeddings. The i-th line shows the i-th word in the tour. Note that the embeddings are circular, and thus the first line and the last line are next to each other.

📝 Results

Examples of segments

Each row represents a segment. (a--d) Segments around "cat." (e--h) Segments around "concept." (i--o) Random segments of WordTour. WordTour provides smooth orderings.

	Methods	1	2	3	4	5	6	7	8	9	10	11
(a)	WordTour	sniff	sniffing	sniffer	dogs	dog	cat	cats	pets	pet	stray	errant
(b)	RandProj	loire	sayings	nn	trooper	referendum	cat	exceeded	traces	freestyle	mirrored	bloomberg
(c)	PCA1	mm	asylum	kohl	presents	expressed	cat	sichuan	denmark	counted	corporations	hewitt
(d)	PCA4	1.46	puzzles	940	coexist	locations	cat	att	winners	perth	colgate	sohail
(e)	WordTour	assumption	assumptions	notions	notion	idea	concept	concepts	ideas	thoughts	feelings	emotions
(f)	RandProj	entertaining	42,000	kursk	embarrassment	ingrained	concept	berezovsky	cg	guillen	excerpts	roofs
(g)	PCA1	neighboring	branches	argued	manhattan	1998	concept	share	pending	response	airlines	fort
(h)	PCA4	2:00	hksar	hashim	provider	straining	concept	inducing	fightback	unsettled	bavaria	sign
(i)	WordTour	wireless	broadband	3g	cdma	gsm	handset	handsets	smartphones	smartphone	blackberry	tablet
(j)	WordTour	gun	weapon	weapons	arms	arm	leg	legs	limbs	limb	prosthetic	make-up
(k)	WordTour	federalist	libertarian	progressive	liberal	conservative	conservatives	liberals	democrats	republicans	gop	republican
(l)	WordTour	cordial	amicable	agreeable	mutually	beneficial	detrimental	harmful	destructive	disruptive	behaviour	behavior
(m)	WordTour	15th	14th	13th	12th	10th	11th	9th	8th	7th	6th	5th
(n)	WordTour	suspicions	doubts	doubt	doubted	doubting	doubters	skeptics	skeptic	believer	believers	adherents
(o)	WordTour	molten	magma	lava	basalt	sandstone	limestone	granite	marble	slab	slabs	prefabricated

Crawdsourcing Evaluation

Each bar represents the number of times each method was selected by crowdworkers within 100 trials.

Document Classification

Each cell reports a kNN classification error. Lower is better. The time row reports the average time to compare the two documents. WordTour performs the best in the blurred BoW family.

	ohsumed	reuter	20news	amazon	classic
BoW	48.1	5.6	35.4	11.4 ± 0.4	5.1 ± 0.3
Time	39 ns	23 ns	35 ns	21 ns	23 ns
WordTour	47.2	4.6	34.1	10.1 ± 0.3	4.6 ± 0.1
RandProj	47.9	5.4	35.4	11.3 ± 0.3	5.1 ± 0.3
PCA1	47.8	5.7	35.5	11.4 ± 0.6	5.1 ± 0.3
PCA4	48.1	5.6	35.4	11.6 ± 0.5	5.1 ± 0.4
Time	206 ns	142 ns	312 ns	185 ns	150 ns
WMD	47.5	4.5	30.7	7.6 ± 0.3	4.2 ± 0.3
Time	3.5 x 10^6 ns	2.2 x 10^6 ns	5.1 x 10^6 ns	1.2 x 10^7 ns	1.9 x 10^6 ns

⛏️ How to Build WordTour by Yourself

1. Install the dependencies.

$ sudo apt install wget unzip build-essential

2. Download the GloVe embeddings and LKH solver.

$ ./download.sh

3. Compile the generator.

$ make

4. Create the LKH config file.

$ ./make_LKH_file
Usage: ./make_LKH_file [embedding file path] [#words]
$ ./make_LKH_file ./glove.6B/glove.6B.300d.txt 400 > ./LKH-3.0.6/wordtour.tsp
$ cp wordtour.par ./LKH-3.0.6/wordtour.par

Here, 400 is used for an illustration purpose. 40000 words may take a few minutes and consume a few GB of strage.

5. Compile the LKH solver.

$ cd LKH-3.0.6
$ make

6. Run the LKH solver.

$ ./LKH wordtour.par

Note: The result is saved in wordtour.out.

Note: It may take several hourds to run the LKH solver with 40000 words.

7. Extract the word list.

$ cd ..
$ python generate_order_file.py
$ cat wordtour.txt

🧪 How to Evaluate

1. Download the Datasets.

You also need to run ./download.sh if you haven't yet.

$ ./download_datasets.sh

2. Preprocess.

$ python ./preprocess.py

3. Evaluate.

Be sure that the embeddings files, wordtour.txt, order_randproj.txt, order_pca1.txt, and order_pca4.txt, contain the embeddings you want to evaluate. It might contain only 400 words if you ran the "How to Build WordTour by Yourself" scripts.

$ python evaluate.py

🖋️ Citation

@inproceedings{sato2022wordtour,
  author    = {Ryoma Sato},
  title     = {Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem},
  booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, {NAACL-HLT}},
  year      = {2022},
}