Word-level information extraction from science and technology announcements corpus based on CRF

2012 ◽  
Author(s):  
Yushu Cao ◽  
Jun Wang ◽  
Lei Li
Keyword(s):  
Information Extraction ◽  
Science And Technology ◽  
Word Level ◽  
Level Information

InfoXtract: A Customizable Intermediate Level Information Extraction Engine

2003 ◽  
Author(s):  
Rohini K. Srihari ◽  
Wei Li ◽  
Cheng Niu ◽  
Thomas Cornell
Keyword(s):  
Information Extraction ◽  
Intermediate Level ◽  
Level Information

scholarly journals Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction

2018 ◽  
Vol 6 ◽  
pp. 451-465 ◽  
Author(s):  
Daniela Gerz ◽  
Ivan Vulić ◽  
Edoardo Ponti ◽  
Jason Naradowsky ◽  
Roi Reichart ◽  
...  
Keyword(s):  
Large Scale ◽  
Language Modeling ◽  
Language Models ◽  
Data Sets ◽  
High Type ◽  
Word Level ◽  
Level Information ◽  
Character Sequences ◽  
Novel Method ◽  
Morphologically Rich Languages

Neural architectures are prominent in the construction of language models (LMs). However, word-level prediction is typically agnostic of subword-level information (characters and character sequences) and operates over a closed vocabulary, consisting of a limited word set. Indeed, while subword-aware models boost performance across a variety of NLP tasks, previous work did not evaluate the ability of these models to assist next-word prediction in language modeling tasks. Such subword-level informed models should be particularly effective for morphologically-rich languages (MRLs) that exhibit high type-to-token ratios. In this work, we present a large-scale LM study on 50 typologically diverse languages covering a wide variety of morphological systems, and offer new LM benchmarks to the community, while considering subword-level information. The main technical contribution of our work is a novel method for injecting subword-level information into semantic word vectors, integrated into the neural language modeling training, to facilitate word-level prediction. We conduct experiments in the LM setting where the number of infrequent words is large, and demonstrate strong perplexity gains across our 50 languages, especially for morphologically-rich languages. Our code and data sets are publicly available.

InfoXtract: A customizable intermediate level information extraction engine

2006 ◽  
Vol 14 (01) ◽  
Author(s):  
ROHINI K. SRIHARI ◽  
WEI LI ◽  
THOMAS CORNELL ◽  
CHENG NIU
Keyword(s):  
Information Extraction ◽  
Intermediate Level ◽  
Level Information

scholarly journals Word-level information influences phonetic learning in adults and infants

Cognition ◽  
2013 ◽  
Vol 127 (3) ◽  
pp. 427-438 ◽  
Author(s):  
Naomi H. Feldman ◽  
Emily B. Myers ◽  
Katherine S. White ◽  
Thomas L. Griffiths ◽  
James L. Morgan
Keyword(s):  
Word Level ◽  
Phonetic Learning ◽  
Level Information

scholarly journals EXPLORING PRETRAINED MODELS FOR JOINT MORPHO-SYNTACTIC PARSING OF RUSSIAN

2020 ◽  
Author(s):  
D. G. Anastasyev ◽  
Keyword(s):  
Joint Model ◽  
Syntactic Parsing ◽  
Word Embeddings ◽  
The Past ◽  
Word Level ◽  
Level Information

In this paper, we build a joint morpho-syntactic parser for Russian. We describe a method to train a joint model which is significantly faster and as accurate as a traditional pipeline of models. We explore various ways to encode the word-level information and how they can affect the parser’s performance. To this end, we utilize learned from scratch character-level word embeddings and grammeme embeddings that have shown state-of-theart results for similar tasks for Russian in the past. We compare them with the pretrained contextualized word embeddings, such as ELMo and BERT, known to lead to the breakthrough in miscellaneous tasks in English. As a result, we prove that their usage can significantly improve parsing quality.

Processing of word-level and paragraph-level information in schizotypy

2003 ◽  
Vol 60 (1) ◽  
pp. 256-257
Author(s):  
M.A. Niznikiewicz ◽  
S.D. Hun ◽  
P.G. Nestor ◽  
C. Dodd ◽  
M.E. Shenton ◽  
...  
Keyword(s):  
Word Level ◽  
Level Information

scholarly journals SWORD: A SAT like prover using word level information

2007 ◽  
Author(s):  
Robert Wille ◽  
Gorschwin Fey ◽  
Daniel Grobe ◽  
Stephan Eggersgluss ◽  
Rolf Drechsler
Keyword(s):  
Word Level ◽  
Level Information
Sign in / Sign up

Export Citation Format

Share Document