Feature Engineering

AbstractState-of-the-art statistical machine translation is based on alignments between phrases – sequences of words in the source and target sentences. The learning step in these systems often relies on alignments …...

Few attempts have been made to inves- tigate the utility of temporal reasoning within machine learning frameworks for temporal relation classification between events in news articles. This paper presents three …...

This paper describes the German-English translation system developed by the ARK re- search group at Carnegie Mellon University for the Sixth Workshop on Machine Trans- lation (WMT11). We present the …...

The performance of NLP classifiers largely depends on the quality of the features considered for prediction (feature engineering). However, as the number of features increases, the more likely overfit- ting …...

In this paper we introduce our contribution to the SemEval-2013 Task 2 on “Sentiment Analysis in Twitter”. We participated in “task B”, where the objective was to build mod- els …...

Expensive feature engineering based on WordNet senses has been shown be useful for document level sentimentto classification. A plausible reason for such a performance improvement is the reduction in data …...

We propose a discriminatively trained re- current neural network (RNN) that pre- dicts the actions for a fast and accurate shift-reduce dependency parser. The RNN uses its output-dependent model struc- …...

This paper describes TwitterHawk, a system for sentiment analysis of tweets which partici- pated in the SemEval-2015 Task 10, Subtasks A through D. The system performed com- petitively, most notably …...

Understanding unstructured text is a ma- jor goal within natural language process- ing. Comprehension tests pose questions based on short text passages to evaluate such understanding. In this work, we …...

Existing work on detecting deceptive reviews primarily focuses on feature engineering and applies off-the-shelf supervised classification algorithms to the problem. Then, one real challenge would be to manually recognize plentiful …...

Most previous approaches to Chinese word segmentation formalize this prob- lem as a character-based sequence label- ing task so that only contextual informa- tion within fixed sized local windows and …...

AbstractThe conventional solution for handling sparsely labelled data is extensive feature engineering. This is time consuming and task and domain specific. We present a novel approach for learning embedded features …...

AbstractWe investigate the value of feature engineering and neural network models for predicting successful writing. Similar to previous work, we treat this as a binary classification task and explore new …...

AbstractNamed Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but …...

With the advent of neural models, there has been a rapid move away from feature engineer- ing, or at best, simplistically combining hand- crafted features with learned representations as side …...

AbstractPeople convey sentiments and emotions through language. To understand these affectual states is an essential step towards understanding natural language. In this paper, we propose a transfer-learning based approach to …...

AbstractMorphological analysis and disambiguation is an important task and a crucial preprocessing step in natural language processing of morphologically rich languages. Kinyarwanda, a morphologically rich language, currently lacks tools for …...

English research articles (RAs) are an essential genre in academia, so the attempts to employ NLP to assist the development of academic writing ability have received considerable attention in the …...

AbstractThis paper describes a system submitted by team BigGreen to LCP 2021 for predicting the lexical complexity of English words in a given context. We assemble a feature engineering-based model …...

AbstractThis paper presents the system we submitted to the first Lexical Complexity Prediction (LCP) Shared Task 2021. The Shared Task provides participants with a new English dataset that includes context …...

AbstractTraditional hand-crafted linguistically-informed features have often been used for distinguishing between translated and original non-translated texts. By contrast, to date, neural architectures without manual feature engineering have been less explored …...

AbstractThis paper presents Imperial College London’s submissions to the WMT21 Quality Estimation (QE) Shared Task 3: Critical Error Detection. Our approach builds on cross-lingual pre-trained representations in a sequence classification …...

AbstractIn conversational analyses, humans manually weave multimodal information into the transcripts, which is significantly time-consuming. We introduce a system that automatically expands the verbatim transcripts of video-recorded conversations using multimodal …...

AbstractFeature engineering is an important step in classical NLP pipelines, but machine learning engineers may not be aware of the signals to look for when processing foreign language text. The …...

AbstractWe present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised …...

Discovering whether words are semantically related and identifying the specific semantic re- lation that holds between them is of crucial im- portance for automatic reasoning on text data. For that …...

AbstractThis paper narrates the work of the team Amrita_CEN for the shared task on Patronizing and Condescending Language Detection at SemEval 2022. We implemented machine learning algorithms such as Support …...

AbstractThis paper describes the University of Helsinki submission to the SemEval 2022 task on multilingual idiomaticity detection. Our system utilizes several models made available by HuggingFace, along with the baseline …...

AbstractFeature Engineering consists in the application of domain knowledge to select and transform relevant features to build efficient machine learning models. In the Natural Language Processing field, the state of …...