Maswali ya Kupachika Neno

Majibu na maelezo huonekana unapojibu maswali.

1. How does a learned word embedding represent a word for numerical comparison?
   • As a plain-text dictionary entry only
   • As a vector of learned numerical features
   • As a complete recorded pronunciation only
   • As a manually assigned grammar rule
2. Je, mifano kama neno2vec hujifunza nini neno linamaanisha?
   • Kwa kusoma fasili zilizoandikwa kwa mkono kutoka kwenye kamusi
   • Kwa kuhesabu herufi katika kila neno
   • Kwa kutazama ni maneno gani mengine huwa yanaonekana karibu nayo kwa idadi kubwa ya maandishi
   • Kwa kuwauliza wakadiriaji wa kibinadamu waweke alama kila neno
3. What does the classic king-minus-man-plus-woman vector example illustrate?
   • Some learned representations capture relationships through vector offsets
   • Every word relation obeys an exact arithmetic law
   • Vector arithmetic verifies every factual statement
   • The vector stores a word's exact spelling only
4. Kuna tofauti gani kuu kati ya upachikaji wa neno2vec/GloVe na upachikaji ndani ya miundo ya kisasa ya kibadilishaji?
   • Transfoma haitumii nambari hata kidogo
   • GloVe hufanya kazi kwenye picha pekee
   • Mifano ya kisasa haiwezi kuwakilisha maana
   • Word2vec inatoa vekta moja fasta kwa kila neno, wakati transfoma hutoa vekta zinazotegemea muktadha
5. Ni kazi gani moja kwa moja inategemea kulinganisha vekta za kupachika?
   • Kuhesabu jumla ya idadi ya maneno katika hati
   • Kubadilisha maandishi kuwa herufi kubwa
   • Utafutaji wa kimantiki unaopata hati zenye maana sawa na swali
   • Kuangalia saizi ya faili kwenye diski
6. Which scale of dimensionality is found in common released classic word-vector configurations?
   • Exactly one coordinate for every model
   • Tens or hundreds of learned coordinates per word
   • Exactly two coordinates in all word2vec models
   • At least ten million coordinates per word by definition
7. In a toy word-vector comparison, what is the cosine similarity of the nonzero vectors one-zero and zero-one?
   • Moja
   • Minus one
   • Mbili
   • Sifuri
8. What should the first coordinate of an arbitrary learned word embedding be assumed to mean?
   • A learned feature without a universal predetermined linguistic label
   • The word's character count in every model
   • A fixed sentiment score in every dataset
   • The document's factual reliability by definition