Context-based word prediction is provided. A software application utilizes words contained in an application document to provide context-based word prediction in the same or a related document. The software application creates an application defined data source and populates the data source with words occurring in a document. When the same or a related document is being edited via an input method, for example, typing, speech recognition, electronic handwriting, etc., a prediction engine presents candidate words from the application defined data source that match current text input, and the user may choose from the presented candidate words for automatic population into the document being edited. Information from the application defined data source may be transferred between computing devices, for example, between a mobile computing device and a desktop (non-mobile) computing device.

Context based word prediction

Computers and computer-based technology have become an integral part of the lives of many individuals with disabilities. One of the most common activities that can be computer assisted is the generation of text. People who cannot accurately control their extremities (due to disabilities such as cerebral palsy and spinal cord injury) use computers as writing tools. People whose physical disability restricts their spoken output may use a computer as a communication prosthesis. In both cases, the generation of text is a necessary activity that can be physically demanding. It should be made as easy for the user as possible. While the standard computer keyboard is an efficient interface for able-bodied people and some disabled people, it may present significant access problems for others. In these cases some alternative interface is necessary.

Generating text from compressed input : an intelligent interface for people with severe motor impairments : Computer & people with disabilities

Embodiments are provided to predict and suggest one or more candidates. Words, acronyms, compound words, phrases, and other textual and symbolic representations can be predicted and suggested to a user as part of an input process or other user operation. In an embodiment, a number of textual candidates can be predicted based in part on user input and data stored in a store component. The number of predicted textual candidates can be suggested to a user as a number of suggested textual candidates. Embodiments enable a user to select an appropriate textual candidate from the number of suggested textual candidates, while reducing a number of associated user operations.

Predicting textual candidates

Word prediction can be used to enhance the communication rate of people with disabilities who use Augmentative and Alternative Communication (AAC) devices. We use statistical methods in a word prediction system, which are trained on a corpus, and then measure the efficacy of the resulting system by calculating the theoretical keystroke savings on some held out data. Ideally training and testing should be done on a large corpus of AAC text covering a variety of topics, but no such corpus exists. We discuss training and testing on a wide variety of corpora meant to approximate text from AAC users. We show that training on a combination of in-domain data with out-of-domain data is often more beneficial than either data set alone and that advanced language modeling such as topic modeling is portable even when applied to very different text.

Corpus studies in word prediction

The paper discusses an application of a technique to tag a corpus containing the English of Finnish Australians automatically and to analyse the frequency vectors of part-ofspeech (POS) trigrams using a permutation test. Our goal is to detect the linguistic sources of the syntactic variation between two groups, the ‘Adults,’ who had received their school education in Finland, and the ‘Juveniles,’ who were educated in Australia. The idea of the technique is to utilise frequency profiles of trigrams of POS categories as indicators of syntactic distance between the groups and then examine potential effects of language contact and language (‘vernacular’) universals in SLA. The results show that some features we describe as ‘contaminating’ the interlanguage of the Adults can be best attributed to Finnish substratum transfer. However, there are other features in our data that may also be ascribed to more “universal” primitives or universal properties of the language faculty. As we have no evidence of potential contamination at the early stages of the Juveniles’ L2 acquisition, we cannot yet prove or refute our hypothesis about the strength of contact influence as opposed to that of the other factors.

/pdf/detecting-syntactic-contamination-in-emigrants-the-english-5g37948ohn.pdf

Detecting Syntactic Contamination in Emigrants: The English of Finnish Emigrants

The problem addressed in the present thesis is the utility of linguistic knowledge in one domain of language technology, word prediction. An important characteristic of any practical language technology application is its level of performance, and it is therefore essential to be able to measure this quantitatively. The main questions in the present thesis are the following: (1) how can a significant improvement in performance be obtained in practical language technology products, and (2) what is the cost of improved performance in terms of the sources of linguistic knowledge that should be incorporated in them? On a more general level, the major findings suggest that the practical utility of linguistic knowledge in language technology should generally be evaluated from at least three larger perspectives: (1) language, (2) technology, and (3) the user of the application. From these three perspectives, a variety of constraints can be identified which either increase or decrease the usefulness of linguistic knowledge in practical language technology applications. A statistical stateof-the-art word prediction system was developed and tested in the empirical part of this work, and testing the performance of a few prediction methods that utilise sources of linguistic knowledge showed that they can perform just as well as some existing state-of-the-art statistical prediction methods. When the syllable-initial characters of the words to be predicted were used, for example, the expected length of the search key in a running text with a prediction list of ten tokens was only 1.59 characters, while the use of information on the parts of speech of the word tokens to be predicted in a system with five lists representing five parts of speech resulted only in a three percent improvement in performance. One of the practical implications of these results for the field of language technology is that a significant improvement in the performance of a word prediction system may be achieved only incrementally. The simultaneous use of several techniques may in turn dilute the real-time operation of the prediction system, so that it is unable to suggest candidate words quickly enough for the user. It can also affect some performance aspects such as the average percentage of keystrokes/characters saved.

http://jultika.oulu.fi/files/isbn951427850X.pdf

Pertti Väyrynen

Papers

Perspectives on the utility of linguistic knowledge in English word prediction