Sentence examples for sentence model from inspiring English sources

As described previously, the LingPipe Indo-European sentence model and Cafetiere sentence splitter were individually applied in this work.

In segmenting documents into sentences, two heuristics-based tools, i.e., the LingPipe Indo-European sentence model [37] and NaCTeM's Cafetiere sentence splitter [38], were individually employed in our experiments.

For the purpose of comparison, we have also provided results obtained by our baseline, i.e., the variant of the named entity recogniser that employs non-specialised pre-processing analytics (i.e., the LingPipe Indo-European sentence model and the GENIA tokeniser) and none of the knowledge-rich features and post-processing heuristics.

> As an initial step to the training of CRF models, the abstracts were pre-processed by sentence splitting [using the MEDLINE sentence model in LingPipe (http://alias-i.com/lingpipe)], tokenization [using OSCAR4 (50)] and part-of-speech and chunk tagging [using GENIA Tagger (51)].

CNN is used in systems for tagging, entity search, sentence modeling, etc. [15 23].

The reduced and alternative sentences model is, nevertheless, a controversial compromise that may not work in other deeply divided societies, such as Kenya, Uganda, the Democratic Republic of Congo, and under the increasingly sharp gaze of the international justice community.

Assume that the sentence "All Model T Fords are black" is true and compare it to the true sentence "All husbands are married".

We used the GeniaSS [26] sentence segmentation model to carry out this comparison.

We present a new dorsal ventral stream framework for language comprehension which unifies basic neurobiological assumptions (Rauschecker & Scott, 2009) with a cross-linguistic neurocognitive sentence comprehension model (eADM; Bornkessel & Schlesewsky, 2006).

Although it has not yet gained any noticeable adoption in the community (partly due to complicated data handling described below), it combines the best of both aforementioned methods: no text-size restrictions, no sentence segmentation model dependencies and negligible memory overhead.

In a nutshell, given a tokenized sentence, each model generates top 10 tagging results with their scores.