Word prediction
Methods to Integrate a Language Model with Semantic Information for a Word Prediction Component
... Most current word prediction systems make use of n-gram language models (LM) to es- timate the probability of the following word in a phrase. In the past years there have been many attempts to enrich ...
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Non Syntactic Word Prediction for AAC
... text prediction. Results indicate that word prediction for unordered message formulation is vi- able using statistical ...accurate prediction for shorter sentences; how- ever, this advantage ...
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Using idiolects and sociolects to improve word prediction
... for word prediction was to use word frequency lists (Swiffin et ...a word unicity point has been reached (the point in a word where there there is no other word with the same ...
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The LAMBADA dataset: Word prediction requiring a broad discourse context
... find word prediction par- ticularly attractive because of its naturalness (it’s easy to norm the data with non-expert humans) and ...likely word given the previ- ous context, following the classic ...
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Adaptive Language Modeling for Word Prediction
... approach avoids the additional data sparseness, it makes an assumption that the topic of discourse only affects the vocabulary usage. Bellegarda (2000) used this approach for LSA-adapted modeling, how- ever, we found ...
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Effectiveness of neural language models for word prediction of textual mammography reports
... Radiologists are required to write free paper paper text reports for breast screenings in order to assign cancer di- agnoses in a later step. The current procedure requires a lot of time and needs efficiency. To ...
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A Stochastic Parser Based on a Structural Word Prediction Model
... main dvi A Stochastic Parser Based on a Structural Word Prediction Model Shinsuke MORI, Masafumi NISHIMURA, Nobuyasu ITOH, Shiho OGINO, Hideo WATANABE IBM Research, Tokyo Research Laboratory, IBM Japa[.] ...
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An Empirical Comparison of Unknown Word Prediction Methods
... first type is based on the concept of supertagging while the second one performs LA. Generally, su- pertagging refers to the process of applying a se- quential tagger to assign lexical descriptions asso- ciated with each ...
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Combining Finite State and Corpus-based Techniques for Unknown Word Prediction
... Let us put it all together. The query for the root is sent but the threshold set for it is 100 because nouns tend to occur less frequently together with the inden- ite article. After a valid root is found, the denite ...
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Target word prediction and paraphasia classification in spoken discourse
... The performance of our language models on the top-n ranked evaluation can be seen in Table 1. The log-bilinear model outperformed the FST in all cases. This finding is similar to state of the art results for automatic ...
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A Classification Approach to Word Prediction
... We present a way that uses external knowledge to generate expressive context representations, along with a learning method capable of handling the large number of features generated this[r] ...
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Beyond Context: A New Perspective for Word Embeddings
... most word embedding models is ...a word also characterizes its ...the word prediction task as a multi- label classification ...of word and context features for constructing word ...
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PreText: A Predictive Text Entry System for Mobile Phones
... in Word Completion Utilities” [2] has proposed the statistical and syntactical prediction of words using part of speech tags and a bigram ...for Word Prediction” [3] has been developed by Ebba ...
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Online Entropy Based Model of Lexical Category Acquisition
... on word prediction (where a missing word is guessed based on its sentential context), semantic inference (where the semantic properties of a novel word are predicted based on the context), and ...
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Increasing Employability and Livelihood of Entirely Paralyzed People using ACAT(Assistive Context Aware Toolkit)
... Assistive Context-Aware Toolkit (ACAT) is an Intel Labs proprietary open source softwarewhich has enabled people like Stephen Hawking with motor neuron diseases and other disabilities to have full access to the ...
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Applying Prediction Techniques to Phoneme based AAC Systems
... of prediction features, including phoneme prediction and word ...neme prediction feature uses a pronunciation dic- tionary to determine which phonemes cannot follow the currently selected ...
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Multi Class Composite N gram Language Model for Spoken Language Processing Using Multiple Word Clusters
... accurate word prediction ca- pability and reliability for sparse data with a compact model size based on multiple word clusters, called Multi- ...as word attributes, and one word ...
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Forecasting Word Model: Twitter based Influenza Surveillance and Prediction
... The increased use of social media platforms has led to wide sharing of personal information. Espe- cially Twitter, a micro-blogging platform that enables users to communicate by updating their status using 140 or fewer ...
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Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks
... tion prediction with a log-linear model based on rich morphological and syntactic ...surface word forms in con- text, similarly to our word BNN, and integrate the scores into the SMT ...
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Formalizing Word Sampling for Vocabulary Prediction as Graph based Active Learning
... To evaluate the accuracy of vocabulary prediction, we used the dataset that Ehara et al. (2010) and Ehara et al. (2012) used. This dataset was gleaned from questionnaires answered by 15 English as a second ...
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