Improving a Strong Neural Parser with Conjunction-Specific Features
نویسندگان
چکیده
While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others. In particular, dependency parsers perform poorly in coordination construction (i.e., correctly attaching the conj relation). We extend a state-of-the-art dependency parser with conjunction-specific features, focusing on the similarity between the conjuncts head words. Training the extended parser yields an improvement in conj attachment as well as in overall dependency parsing accuracy on the Stanford dependency conversion of the Penn TreeBank.
منابع مشابه
Studying impressive parameters on the performance of Persian probabilistic context free grammar parser
In linguistics, a tree bank is a parsed text corpus that annotates syntactic or semantic sentence structure. The exploitation of tree bank data has been important ever since the first large-scale tree bank, The Penn Treebank, was published. However, although originating in computational linguistics, the value of tree bank is becoming more widely appreciated in linguistics research as a whole. F...
متن کاملFeature Engineering in Persian Dependency Parser
Dependency parser is one of the most important fundamental tools in the natural language processing, which extracts structure of sentences and determines the relations between words based on the dependency grammar. The dependency parser is proper for free order languages, such as Persian. In this paper, data-driven dependency parser has been developed with the help of phrase-structure parser fo...
متن کاملAbstract Meaning Representation Parsing using LSTM Recurrent Neural Networks
We present a system which parses sentences into Abstract Meaning Representations, improving state-of-the-art results for this task by more than 5%. AMR graphs represent semantic content using linguistic properties such as semantic roles, coreference, negation, and more. The AMR parser does not rely on a syntactic preparse, or heavily engineered features, and uses five recurrent neural networks ...
متن کاملClassifying responses on online discussion forums
There are online discussions forums covering an endless variety of topics. Each forum has many topic-specific questions with expert answers. However, the best answer is often buried somewhere in the discussion thread. Given a question on an online forum, we focus on the problem of classifying each response to the question as an answer or a non-answer. Where the answer label indicates if the res...
متن کاملShift-Reduce Constituent Parsing with Neural Lookahead Features
Transition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a parser stack, which spans over non-local constituents. On the other hand, during incremental parsing, constituent information on the right hand side of the current word is not utilized, which is a relative weakness of ...
متن کامل