UNT-Yahoo: SuperSenseLearner: Combining SenseLearner with SuperSense and other Coarse Semantic Features

نویسندگان

  • Rada Mihalcea
  • Andras Csomai
  • Massimiliano Ciaramita
چکیده

We describe the SUPERSENSELEARNER system that participated in the English allwords disambiguation task. The system relies on automatically-learned semantic models using collocational features coupled with features extracted from the annotations of coarse-grained semantic categories generated by an HMM tagger.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED)

The SemEval 2016 DiMSUM Shared Task concerns the detection of minimal semantic units from text and prediction of their coarse lexical categories known as supersenses. Our approach is to define this task as a binary classification problem approachable by straightforward machine learning methods. We start by detecting semantic units by matching text spans against several large dictionaries, inclu...

متن کامل

Coarse Semantic Classification of Rare Nouns Using Cross-Lingual Data and Recurrent Neural Networks

The paper presents a method for WordNet supersense tagging of Sanskrit, an ancient Indian language with a corpus grown over four millenia. The proposed method merges lexical information from Sanskrit texts with lexicographic definitions from Sanskrit-English dictionaries, and compares the performance of two machine learning methods for this task. Evaluation concentrates on Vedic, the oldest lay...

متن کامل

Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization

Coarse-grained semantic categories such as supersenses have proven useful for a range of downstream tasks such as question answering or machine translation. To date, no effort has been put into integrating the supersenses into distributional word representations. We present a novel joint embedding model of words and supersenses, providing insights into the relationship between words and superse...

متن کامل

Coarse Lexical Semantic Annotation with Supersenses: An Arabic Case Study

“Lightweight” semantic annotation of text calls for a simple representation, ideally without requiring a semantic lexicon to achieve good coverage in the language and domain. In this paper, we repurpose WordNet’s supersense tags for annotation, developing specific guidelines for nominal expressions and applying them to Arabic Wikipedia articles in four topical domains. The resulting corpus has ...

متن کامل

Description and Results of the SuperSense Tagging Task

SuperSense tagging (SST) is a Natural Language Processing task that consists in annotating each significant entity in a text, like nouns, verbs, adjectives and adverbs, within a general semantic taxonomy defined by the WordNet lexicographer classes (called SuperSenses). SST can be considered as a task half-way between Named-Entity Recognition (NER) and Word Sense Disambiguation (WSD): it is an ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2007