Sentence Realisation from Bag of Words with Dependency Constraints
نویسندگان
چکیده
In this paper, we present five models for sentence realisation from a bag-of-words containing minimal syntactic information. It has a large variety of applications ranging from Machine Translation to Dialogue systems. Our models employ simple and efficient techniques based on n-gram Language modeling. We evaluated the models by comparing the synthesized sentences with reference sentences using the standard BLEU metric(Papineni et al., 2001). We obtained higher results (BLEU score of 0.8156) when compared to the state-of-art results. In future, we plan to incorporate our sentence realiser in Machine Translation and observe its effect on the translation accuracies.
منابع مشابه
Extraction of Drug-Drug Interaction from Literature through Detecting Linguistic-based Negation and Clause Dependency
Extracting biomedical relations such as drug-drug interaction (DDI) from text is an important task in biomedical NLP. Due to the large number of complex sentences in biomedical literature, researchers have employed some sentence simplification techniques to improve the performance of the relation extraction methods. However, due to difficulty of the task, there is no noteworthy improvement in t...
متن کاملDependency Tree-based Sentiment Classification using CRFs with Hidden Variables
In this paper, we present a dependency treebased method for sentiment classification of Japanese and English subjective sentences using conditional random fields with hidden variables. Subjective sentences often contain words which reverse the sentiment polarities of other words. Therefore, interactions between words need to be considered in sentiment classification, which is difficult to be ha...
متن کاملUsing Dependency Parses to Augment Feature Construction for Text Mining
(ABSTRACT) With the prevalence of large data stored in the cloud, including unstructured information in the form of text, there is now an increased emphasis on text mining. A broad range of techniques are now used for text mining, including algorithms adapted from machine learning, NLP, computational linguistics, and data mining. Applications are also multi-fold, including classification, clust...
متن کاملInput Seed Features for Guiding the Generation Process: A Statistical Approach for Spanish
In this paper we analyse a statistical approach for generating Spanish sentences focused on the surface realisation stage guided by an input seed feature. This seed feature can be anything such as a word, a phoneme, a sentiment, etc. Our approach attempts to maximise the appearance of words with that seed feature along the sentence. It follows three steps: first we train a language model over a...
متن کاملGrounded Compositional Semantics for Finding and Describing Images with Sentences
Previous work on Recursive Neural Networks (RNNs) shows that these models can produce compositional feature vectors for accurately representing and classifying sentences or images. However, the sentence vectors of previous models cannot accurately represent visually grounded meaning. We introduce the DTRNN model which uses dependency trees to embed sentences into a vector space in order to retr...
متن کامل