9 Matching Annotations
  1. Dec 2019
    1. The quality of word representations is generally gauged by its ability to encode syntactical information and handle polysemic behavior (or word senses). These properties result in improved semantic word representations. Recent approaches in this area encode such information into its embeddings by leveraging the context. These methods provide deeper networks that calculate word representations as a function of its context.
      • Syntactical information
      • Polysemic behavior (word senses)
      • Semantic word representations

      Entendo que lidar com word senses significa dizer que a representação das palavras consegue medidas similares para palavras similares.

      O que seria informação sintática? E sua relação com representações semânticas da palavra?

    2. Traditional word embedding algorithms assign a distinct vector to each word. This makes them unable to account for polysemy. In a recent work, Upadhyay et al. (2017) provided an innovative way to address this deficit. The authors leveraged multilingual parallel data to learn multi-sense word embeddings.
      • multilingual parallel data
      • multi-sense word embeddings
    3. This is very important as training embeddings from scratch requires large amount of time and resource. Mikolov et al. (2013) tried to address this issue by proposing negative sampling which is nothing but frequency-based sampling of negative terms while training the word2vec model.

      Amostragem negativa... termos negativos?

    4. A general caveat for word embeddings is that they are highly dependent on the applications in which it is used. Labutov and Lipson (2013) proposed task specific embeddings which retrain the word embeddings to align them in the current task space.

      Acredito que aplicação aqui se relaciona com contexto, logo word embeddings são dependentes de contexto. Isso é bem óbvio, a princípio. Seria isso o que o autor quis dizer?

      Retreinar as incorporações para alinhar à tarefa corrente. Alinhar seria nada mais do que adequar as incorporações prévias no novo contexto, é isso?

    5. One solution to this problem, as explored by Mikolov et al. (2013), is to identify such phrases based on word co-occurrence and train embeddings for them separately. More recent methods have explored directly learning n-gram embeddings from unlabeled data (Johnson and Zhang, 2015).

      Co-ocorrência de palavras eu consigo entender, mas treinar as embeddings separadamente não. Seria supor a co-ocorrência das palavras como unidade na incorporação, em vez da palavra apenas?

    6. bigram language model.

      Não sei, ainda, o que significa:

      • bigram
      • language model
    7. The context words are assumed to be located symmetrically to the target words within a distance equal to the window size in both directions.

      O que significa dizer "simetricamente localizadas" as palavras alvo?

    8. This led to the motivation of learning distributed representations of words existing in low-dimensional space (Bengio et al., 2003).

      Sobre maldição da dimensionalidade. Agora, o que seria representações distribuídas das palavras em espaços de menor dimensão? Isso me lembra de PCA e afins.

    1. The word vector is the arrow from the point where all three axes intersect to the end point defined by the coordinates.

      The three axes gives each one a context.