[PDF] Top 20 Natural Language Generation from Pictographs

... Many pictograph systems are in place. Although differences exist across pictograph sets, some fea- tures are shared among them. A pictograph of an entity (noun) can stand for one or multiple in- stances of that entity. ... See full document

... in natural language generation and it is out of our scope to model all the facets and details of direct/repetition priming in the alignment of linguistic ...results from two basic activation ... See full document

... To ensure that SCRISP chooses to generate these adjectives correctly, we follow a class-based approach to the premodifier ordering problem (Mitchell, 2009). In our lexicon we assign adjec- tives denoting spatial ... See full document

... Multiple Goals. We first evaluate STRUCT’s ability to accomplish multiple communicative goals when generating a single sentence. In this experiment, we modify the problem from the pre- vious section. In that ... See full document

... To avail good quality output text from the system, one must conform to the requirement specified in Section 3.2. The NLG system will perform accu- rately if all attributes present in the training tuple- formed ... See full document

... narratives generation in text format has undergone a constant development in the last decades, in research of all the aspects related to natural ...far from showing subjective aspects like emotions ... See full document

... more natural than in the E2E challenge case, 2) there is a large amount of vari- ation in the dataset, and 3) the dataset was split in such a way that the paired set contains perfect matches between the MR and the ... See full document

... Each network is then split into subnetworks by the split network module. This partitions the net- work by locating ‘proposition’ objects (marked with a double-lined box in figure 4) which have no parent and tracing the ... See full document

... The iterative development process of SimpleNLG-NL answered research questions R1.1, R1.2 and R1.3. The development method showed a way to implement Dutch grammar by evaluating the results intermediately based on ... See full document

... entities from DBpedia during the extraction of relations, and we also collect descriptions of instrument types where available, and add them to the Methodius data to provide richer ... See full document

... Adapting MUMBLE Experience with Natural Language Generation A d a p t i n g M U M B L E E x p e r i e n c e w i t h N a t u r a l L a n g u a g e G e n e r a t i o n R o b e r t R u b i n o f f C o m[.] ... See full document

... A Framework for Lexical Selection in Natural Language Generation A ~,"e'aa~)lework ~bv L e x k a t Setecth~n h~ Natm?a~ ge~;vei Nire~tburg Ca, r ~ e g i e , M e l # m U n i v e ~ ' i ~ y /~'" ~;" N i[.] ... See full document

... The first two systems, called NLG1 and NLG2, require a corpus marked only with domainspecific semantic attributes, while the last system, called NLG3, requires a corpus marked with both [r] ... See full document

... For example, in response to requests for definitions, the constituency schema is selected when the relevant knowledge pool contains a rich description of the questioned object's sub-clas[r] ... See full document

... different from Shaw and Hatzivassiloglou’s: we simply compare raw token counts and take the larger value, while they applied a significance test to es- timate the probability that a difference between counts arose ... See full document

... Converting natural language query into test scripts reduces the effort of the test engineer by finding relevant procedures in already existing ...a natural language query and converts the ... See full document

... (HH) from Carnegie Mellon ...differs from human-computer interaction which is our true target domain. From this raw text, an LDA parser (Bangalore and Joshi, 1999) trained using the XTAG-based Penn ... See full document

... a natural language ...return from a linked open data ...returned from the query when the SPARQL query was executed against the triple store because the specific list of triples is subject to ... See full document

... 2013). We perform experiments at generating lan- guage at the word as well as character-level. The CMU−SE dataset consists of 44,016 sentences with a vocabulary of 3,122 words, while the Penn Treebank consists of 42,068 ... See full document

... We present our experimental results in Table 1 and 2. As shown, our model, NLG-LM, outperforms the baseline models in all 5 datasets. In E2E-NLG dataset, it achieves 2.2% higher BLEU score and 0.013 higher NIST score ... See full document