Hybrid Model

This paper presents an integrated approach for Chinese word segmentation, which can perform disambiguation and unknown word identification simultaneously on the input. In this work, a hybrid model is used …...

. Machine transliteration is an automatic method to generate characters or words in one alphabetical system for the corresponding characters in another alphabetical system. There has been increasing concern on …...

This paper proposes a hybrid Chinese named entity recognition model based on multiple features. It differentiates from most of the previous approaches mainly as follows. Firstly, the proposed Hybrid Model …...

We use logical inference techniques for recognising textual entailment. As the performance of theorem proving turns out to be highly dependent on not read- ily available background knowledge, we incorporate …...

Markov order-1 conditional random fields (CRFs) and semi-Markov CRFs are two popular models for sequence segmenta- tion and labeling. Both models have ad- vantages in terms of the type of …...

This paper describes an extremely lexi- calized probabilistic model for fast and accurate HPSG parsing. In this model, the probabilities of parse trees are de- fined with only the probabilities …...

Named entity recognition (NER) has been extensively studied for the names of genes and gene products but there are few proposed solutions for phenotypes. Phenotype terms are expected to play …...

This paper presents a hybrid model for han- dling out-of-vocabulary words in Japanese- to-English statistical machine translation output by exploiting parallel corpus. As the Japanese writing system makes use of …...

We investigate a combination of a tra- ditional linear sparse feature model and a multi-layer neural network model for deterministic transition-based dependency parsing, by integrating the sparse features into the …...

We present a description of the system submit- ted to the Semantic Textual Similarity (STS) shared task at SemEval 2016. The task is to assess the degree to which two …...

AbstractThis paper presents a deep neural solver to automatically solve math word problems. In contrast to previous statistical learning approaches, we directly translate math word problems to equation templates using …...

AbstractTemporality is crucial in understanding the course of clinical events from a patient’s electronic health recordsand temporal processing is becoming more and more important for improving access to content. SemEval …...

AbstractDeduplication is the task of identifying near and exact duplicate data items in a collection. In this paper, we present a novel method for deduplication of scholarly documents. We develop …...

AbstractThis paper presents our work on LegalEval (understanding legal text), one of the tasks in SemEval-2023. It comprises of three sub-tasks namely Rhetorical Roles (RR), Legal Named Entity Recognition (L-NER), …...