1D-Touch: NLP-Assisted Coarse Text Selection via a Semi-Direct Gesture
Peiling Jiang Li Feng Fuling Sun Parakrant Sarkar Haijun Xia Can Liu
Existing text selection techniques on touchscreen focus on improving the
control for moving the carets. Coarse-grained text selection on word and phrase
levels has not received much support beyond word-snapping and entity
recognition. We introduce 1D-Touch, a novel text selection method that
complements the carets-based sub-word selection by facilitating the selection
of semantic units of words and above. This method employs a simple vertical
slide gesture to expand and contract a selection area from a word. The
expansion can be by words or by semantic chunks ranging from sub-phrases to
sentences. This technique shifts the concept of text selection, from defining a
range by locating the first and last words, towards a dynamic process of
expanding and contracting a textual semantic entity. To understand the effects
of our approach, we prototyped and tested two variants: WordTouch, which offers
a straightforward word-by-word expansion, and ChunkTouch, which leverages NLP
to chunk text into syntactic units, allowing the selection to grow by
semantically meaningful units in response to the sliding gesture. Our
evaluation, focused on the coarse-grained selection tasks handled by 1D-Touch,
shows a 20% improvement over the default word-snapping selection method on
Android.