| --- |
| license: cc-by-nc-4.0 |
| library_name: gensim |
| tags: |
| - word2vec |
| - word-embeddings |
| - gensim |
| - earnings-calls |
| - finance |
| - nlp |
| --- |
| |
| # Earnings-Call Word2Vec |
|
|
| Word2Vec word embeddings (gensim, CBOW, 300 dimensions, window 5) trained on the |
| question-and-answer portions of quarterly earnings-call transcripts. The repository |
| holds one model per year (2022-2025); each year continues training from the previous |
| year's model. The 2025 bigram and trigram phrase models are included so that new text |
| can be preprocessed into the same multi-word tokens that appear in the vocabulary |
| (multi-word terms are joined with an underscore, e.g. `supply_chain`). |
|
|
| These embeddings are general purpose. Measuring corporate culture (Li, Mai, Shen, and |
| Yan, 2021) is one downstream use; the vectors can be applied to any task over |
| earnings-call or similar business text. |
|
|
| ## Files |
|
|
| | File | Contents | |
| |------|----------| |
| | `word2vec_YYYY.kv` (+ `.kv.vectors.npy`) | KeyedVectors for year `YYYY` (2022-2025): word vectors + vocabulary | |
| | `phrases_bigram_2025.mod` | gensim Phrases model that joins two-word phrases | |
| | `phrases_trigram_2025.mod` | gensim Phrases model that joins three-word phrases | |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| from gensim.models import KeyedVectors |
| |
| # KeyedVectors.load needs the .vectors.npy file alongside the .kv file |
| kv_path = hf_hub_download("maifeng/earnings-call-word2vec", "word2vec_2025.kv") |
| hf_hub_download("maifeng/earnings-call-word2vec", "word2vec_2025.kv.vectors.npy") |
| kv = KeyedVectors.load(kv_path) |
| kv.most_similar("innovation") |
| ``` |
|
|
| To tokenize new text the same way before looking up vectors, apply the phrase models: |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| from gensim.models.phrases import Phrases |
| |
| bigram = Phrases.load(hf_hub_download("maifeng/earnings-call-word2vec", "phrases_bigram_2025.mod")) |
| trigram = Phrases.load(hf_hub_download("maifeng/earnings-call-word2vec", "phrases_trigram_2025.mod")) |
| tokens = trigram[bigram["the supply chain was disrupted".split()]] |
| ``` |
|
|
| ## Method and reference |
|
|
| The training pipeline (preprocessing, phrase detection, Word2Vec, seed-word dictionary |
| expansion) is available as the [`lmsy_w2v_rfs`](https://github.com/maifeng/lmsy_w2v_rfs) |
| package. |
|
|
| > Kai Li, Feng Mai, Rui Shen, and Xinyan Yan, "Measuring Corporate Culture Using Machine |
| > Learning," *The Review of Financial Studies*, 2021. DOI: |
| > [10.1093/rfs/hhaa079](https://doi.org/10.1093/rfs/hhaa079). |
|
|
| ## License |
|
|
| Released under [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/): |
| non-commercial reuse and redistribution are permitted with attribution to the |
| reference above. |
|
|
