In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

[Q]: These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text.
Question: How do they extract topics?
[A]: automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15

[Q]: To overcome the above-mentioned challenges, we propose a locally-linear meta-embedding learning method that (a) requires only the words in the vocabulary of each source embedding, without having to predict embeddings for missing words, (b) can meta-embed source embeddings with different dimensionalities, (c) is sensitive to the diversity of the neighbourhoods of the source embeddings.

Our proposed method comprises of two steps: a neighbourhood reconstruction step (Section "Nearest Neighbour Reconstruction" ), and a projection step (Section "Projection to Meta-Embedding Space" ). In the reconstruction step, we represent the embedding of a word by the linearly weighted combination of the embeddings of its nearest neighbours in each source embedding space.
Question: What is the introduced meta-embedding method introduced in this paper?
[A]: proposed method comprises of two steps: a neighbourhood reconstruction step (Section "Nearest Neighbour Reconstruction" ), and a projection step (Section "Projection to Meta-Embedding Space" ). In the reconstruction step, we represent the embedding of a word by the linearly weighted combination of the embeddings of its nearest neighbours in each source embedding space.

[Q]: . It is evident from Table TABREF17 that GM$\_$KL achieves better correlation than existing approaches for various metrics on SCWS dataset.
Question: How does this approach compare to other WSD approaches employing word embeddings?
[A]:
GM$\_$KL achieves better correlation than existing approaches for various metrics on SCWS dataset.