Word Embedding Techniques signify a set of language modeling and feature learning methods in natural language processing (NLP) where words from a vocabulary are mapped to vectors in a low-dimensional space. Its unique characteristics are keys that unlock efficient representation and treatment of words and phrases in extensive computational platforms.

Highly Convenient: These techniques encapsulate an accessible methodology, effectively remapping complex language data into a form that is manageable, comprehensible, and adaptable to various algorithms.

Captures Meaning: Word vectors derived from these techniques not only capture individual word meanings but can also discern syntactic and semantic relationships between words.

Enables Dimensionality Reduction: Cultivating a lower-dimensional latent space helps in addressing the curse of dimensionality, thus enhancing computational efficiency.

Engages in Unsupervised Learning: Trained on large amounts of unannotated text data, these techniques undertake unsupervised learning, requiring no expensive annotation effort.

Exploits Context: Utilizing the context of the words to predict the target words (and vice versa) forms the crux of training algorithms like Word2Vec, GloVe, etc.

Typically, variety sectors utilize Word Embedding Techniques to comprehend and make sensible predictions in large text data. It's widely used in document clustering, text classification, sentiment analysis, named entity recognition, and part of speech tagging.

Given these pros and cons, it's crucial to understand when to employ Word Embedding Techniques. They are especially useful when dealing with large text datasets, where identifying the semantic and syntactic relationships among words is vital. However, if the text data is relatively small with limited vocabulary, traditional text representation methods may suffice.

Adopting Word Embedding Techniques involves careful selection of the appropriate embedding method (like Skip-Gram or Continuous Bag of Words in Word2Vec, GloVe, FastText, etc.) tailored precisely to cater to the organizational needs. Further, selecting the number of dimensions in the embedded space and tuning other associated hyperparameters is a critical step. Monitoring performance and training these models on the suitably selected corpus is paramount to successful implementation.

• Cost-Effective: In terms of computational resources, word embedding techniques are more cost-effective than traditional bag-of-words model or one-hot encoding since they reduce dimensionality, saving both time and computational resources.

• Scalability: They scale well with the size of the vocabulary, rendering them feasible even on large text corpora.

• Captures Contextual Semantics: The embedded vectors capture semantics and understand contexts, including synonyms, antonyms, and other nuances involved.

• Robust: These methods are resilient to misused or misspelled words since they can group similar words nearby in the vector space.

Despite their varied advantages, understanding the potential drawbacks of Word Embedding Techniques is equally essential:

• Limited Interpretability: While these techniques can discern a high level of semantic relationships, they often prove difficult to interpret.

• Storage Constraints: For extensive vocabularies, word embedding can demand significant storage resources.

• Lack of Understanding of Word Ambiguity: Word embedding techniques often neglect word ambiguity, assigning the same vector to a word regardless of its contextual usage.

• Risk of Bias: Since these methods learn from existing text data, there's a risk of inheriting prejudices, stereotypes, and biases encoded within.

In conclusion, Word Embedding Techniques are formidable tools in NLP, transforming linguistic data into numerical data without losing the underlying semantic meanings. While they come with inherent challenges, their judicious usage can open up new vistas for significant improvement in NLP tasks.