Abstract: A water collection device is described. In one embodiment, a water collection device includes a collection tube having an inner wall, an open mouth at its upper end, and a closed bottom end, the bottom end including an upwardly projecting sidewall defining an interior projection at least partly spaced apart from the inner wall. The water collection device also includes a float within the collection tube. The float has an upper end and a lower end, the lower end having an axial bore sized to fit over the interior projection defined by the upwardly projecting sidewall.

Abstract: A water collection device is described. In one embodiment, a water collection device includes a collection tube having an inner wall, an open mouth at its upper end, and a closed bottom end, the bottom end including an upwardly projecting sidewall defining an interior projection at least partly spaced apart from the inner wall. The water collection device also includes a float within the collection tube. The float has an upper end and a lower end, the lower end having an axial bore sized to fit over the interior projection defined by the upwardly projecting sidewall.

Abstract: A natural language processing system is disclosed herein. Embodiments of the NLP system perform hand-written rule-based operations that do not rely on a trained corpus. Rules can be added or modified at any time to improve accuracy of the system, and to allow the same system to operate on unstructured plain text from many disparate contexts (e.g. articles as well as twitter contexts as well as medical articles) without harming accuracy for any one context. Embodiments also include a language decoder (LD) that generates information which is stored in a three-level framework (word, clause, phrase). The LD output is easily leveraged b various software applications to analyze large quantities of text from any source in a more sophisticated and flexible manner than previously possible. A query language (LDQL) for information extraction from NLP parsers' output is disclosed, with emphasis on on its embodiment implemented for LD.

Abstract: A natural language processing system is disclosed herein. Embodiments of the NLP system perform hand-written rule-based operations that do not rely on a trained corpus. Rules can be added or modified at any time to improve accuracy of the system, and to allow the same system to operate on unstructured plain text from many disparate contexts (e.g. articles as well as twitter contexts as well as medical articles) without harming accuracy for any one context. Embodiments also include a language decoder (LD) that generates information which is stored in a three-level framework (word, clause, phrase). The LD output is easily leveraged by various software applications to analyze large quantities of text from any source in a more sophisticated and flexible manner than previously possible. A query language (LDQL) for information extraction from NLP parsers' output is disclosed, with emphasis on its embodiment implemented for LD.

Abstract: A natural language processing system is disclosed herein. Embodiments of the NLP system perform hand-written rule-based operations that do not rely on a trained corpus. Rules can be added or modified at any time to improve accuracy of the system, and to allow the same system to operate on unstructured plain text from many disparate contexts (e.g. articles as well as twitter contexts as well as medical articles) without harming accuracy for any one context. Embodiments also include a language decoder (LD) that generates information which is stored in a three-level framework (word, clause, phrase). The LD output is easily leveraged by various software applications to analyze large quantities of text from any source in a more sophisticated and flexible manner than previously possible. A query language (LDQL) for information extraction from NLP parsers' output is disclosed, with emphasis on its embodiment implemented for LD.