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https://github.com/joisino/wordtour

Code for "Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem" (NAACL 2022)
https://github.com/joisino/wordtour

embeddings machine-learning natural-language-processing word-embeddings word2vec

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

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# 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



![Illustration](./imgs/illust.png)



## 💡 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.



![Crownsourcing Evaluation](./imgs/user_study.png)



### 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},

}

```