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.

At the intersection of computational linguistics and artificial intelligence is where we find natural language processing. Very broadly, natural language processing (NLP) is a discipline which is interested in how human languages, and, to some extent, the humans who speak them, interact with technology. NLP is an interdisciplinary topic which has historically been the equal domain of artificial intelligence researchers and linguistics alike; perhaps obviously, those approaching the discipline from the linguistics side must get up to speed on technology, while those entering the discipline from the technology realm need to learn the linguistic concepts. It is this second group that this post aims to serve at an introductory level, as we take a no-nonsense approach to defining some key NLP terminology. While you certainly won't be a linguistic expert after reading this, we hope that you are better able to understand some of the NLP-related discourse, and gain perspective as to how to proceed with learning more on the topics herein.

Very broadly, natural language processing (NLP) is a discipline which is interested in how human languages, and, to some extent, the humans who speak them, interact with technology. If a document collection's words are ordered by frequency, and y is used to describe the number of times that the xth word appears, Zipf's observation is concisely captured as y cx-1/2 (item frequency is inversely proportional to item rank). Also known as meaning generation, semantic analysis is interested in determining the meaning of text selections (either character or word sequences). After an input selection of text is read and parsed (analyzed syntactically), the text selection can then be interpreted for meaning.

At the intersection of computational linguistics and artificial intelligence is where we find natural language processing. Very broadly, natural language processing (NLP) is a discipline which is interested in how human languages, and, to some extent, the humans who speak them, interact with technology. NLP is an interdisciplinary topic which has historically been the equal domain of artificial intelligence researchers and linguistics alike; perhaps obviously, those approaching the discipline from the linguistics side must get up to speed on technology, while those entering the discipline from the technology realm need to learn the linguistic concepts. It is this second group that this post aims to serve at an introductory level, as we take a no-nonsense approach to defining some key NLP terminology. While you certainly won't be a linguistic expert after reading this, we hope that you are better able to understand some of the NLP-related discourse, and gain perspective as to how to proceed with learning more on the topics herein.