instruction:
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
question:
The system consists of: (i) Local, shallow features based mostly on orthographic, word shape and n-gram features plus their context; and (ii) three types of simple clustering features, based on unigram matching: (i) Brown BIBREF32 clusters, taking the 4th, 8th, 12th and 20th node in the path; (ii) Clark BIBREF33 clusters and, (iii) Word2vec BIBREF34 clusters, based on K-means applied over the extracted word vectors using the skip-gram algorithm.
answer:
What shallow local features are extracted?

question:
Car-speak is abstract language that pertains to a car's physical attribute(s). In this instance the physical attributes that the term "fast" pertains to could be the horsepower, or it could be the car's form factor (how the car looks). However, we do not know exactly which attributes the term "fast" refers to.
answer:
How does car speak pertains to a car's physical attributes?

question:
For our single-label topic classification experiments, we use the Switchboard Telephone Speech Corpus BIBREF21 , a collection of two-sided telephone conversations. We further evaluate our topic ID performance on the speech corpora of three languages released by the DARPA LORELEI (Low Resource Languages for Emergent Incidents) Program.
answer:
What datasets are used to assess the performance of the system?