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doc2vec, generates distributed representations of variable-length pieces of texts, such as sentences, paragraphs, or entire documents. [14] [15] doc2vec has been implemented in the C, Python and Java/Scala tools (see below), with the Java and Python versions also supporting inference of document embeddings on new, unseen documents.
Exceeds accuracy of Brill94 tbl tagger using default training files; MontyREChunker: chunks tagged text into verb, noun, and adjective chunks (VX, NX, and AX respectively) MontyExtractor: extracts verb-argument structures, phrases, and other semantically valuable information from sentences and returns sentences as "digests"
a TL dictionary - needed by the target language morphological generator to generate target language words. [9] The RBMT system makes use of the following: a Source Grammar for the input language which builds syntactic constructions from input sentences; a Source Lexicon which captures all of the allowable vocabulary in the domain;
A lot of work has appeared offering different modifications of this algorithm. These works use other resources for analysis (thesauruses, synonyms dictionaries or morphological and syntactic models): for instance, it may use such information as synonyms, different derivatives, or words from definitions of words from definitions. [7]
SPL (Sentence Plan Language) is an abstract notation representing the semantics of a sentence in natural language. [1] In a classical Natural Language Generation (NLG) workflow, an initial text plan (hierarchically or sequentially organized factoids, often modelled in accordance with Rhetorical Structure Theory) is transformed by a sentence planner (generator) component to a sequence of ...
A sentence diagram is a pictorial representation of the grammatical structure of a sentence. The term "sentence diagram" is used more when teaching written language, where sentences are diagrammed. The model shows the relations between words and the nature of sentence structure and can be used as a tool to help recognize which potential ...
The algorithm can be formulated as comparing the top two tokens of the stack (after adding the next token to the stack) or the top token on the stack and the next token in the sentence. Training data for such an algorithm is created by using an oracle, which constructs a sequence of transitions from gold trees which are then fed to a classifier ...
Then the sentences can be ranked with regard to their similarity to this centroid sentence. A more principled way to estimate sentence importance is using random walks and eigenvector centrality. LexRank [19] is an algorithm essentially identical to TextRank, and both use this approach for document summarization. The two methods were developed ...
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