Stacked Sentence-Document Classifier Approach for Improving Native Language Identification
نویسندگان
چکیده
In this paper, we describe the approach of the ItaliaNLP Lab team to native language identification and discuss the results we submitted as participants to the essay track of NLI Shared Task 2017. We introduce for the first time a 2-stacked sentencedocument architecture for native language identification that is able to exploit both local sentence information and a wide set of general–purpose features qualifying the lexical and grammatical structure of the whole document. When evaluated on the official test set, our sentence-document stacked architecture obtained the best result among all the participants of the essay track with an F1 score of 0.8818.
منابع مشابه
Bharathi SSN @ INLI-FIRE-2017: SVM based approach for Indian Native Language Identification
Native Language Identification (NLI) is the task of identifying the native language of a writer or a speaker by analyzing their text. NLI can be important for a number of applications. In forensic linguistics, native language is often used as an important feature for authorship profiling and identification. Nowadays due to the huge usage of social media sites and online interactions, receiving ...
متن کاملNative Language Identification using Stacked Generalization
Ensemble methods using multiple classifiers have proven to be the most successful approach for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of ...
متن کاملCan characters reveal your native language? A language-independent approach to native language identification
A common approach in text mining tasks such as text categorization, authorship identification or plagiarism detection is to rely on features like words, part-of-speech tags, stems, or some other high-level linguistic features. In this work, an approach that uses character n-grams as features is proposed for the task of native language identification. Instead of doing standard feature selection,...
متن کاملShortcut-Stacked Sentence Encoders for Multi-Domain Inference
We present a simple sequential sentence encoder for multi-domain natural language inference. Our encoder is based on stacked bidirectional LSTM-RNNs with shortcut connections and fine-tuning of word embeddings. The overall supervised model uses the above encoder to encode two input sentences into two vectors, and then uses a classifier over the vector combination to label the relationship betwe...
متن کاملAIDA2: A Hybrid Approach for Token and Sentence Level Dialect Identification in Arabic
In this paper, we present a hybrid approach for performing token and sentence levels Dialect Identification in Arabic. Specifically we try to identify whether each token in a given sentence belongs to Modern Standard Arabic (MSA), Egyptian Dialectal Arabic (EDA) or some other class and whether the whole sentence is mostly EDA or MSA. The token level component relies on a Conditional Random Fiel...
متن کامل