raw text
From Raw Text to Universal Dependencies Look, No Tags!
... from raw text to universal ...a raw text, and a parser, which builds a dependency tree over the words of each sentence, without relying on part-of-speech tags or any other explicit ...
11
Simple Unsupervised Grammar Induction from Raw Text with Cascaded Finite State Models
... The results from the cascaded PRLG chunker are near or better than the best performance by CCL or CCM in these experiments. These and the full-length parsing results suggest that the cascaded chunker strategy generalizes ...
10
Multilingual Universal Dependency Parsing from Raw Text with Low Resource Language Enhancement
... the raw text for input and chooses the corresponding model for a particular test set with a model ...the raw text, the system finally out- puts the syntactic dependencies in the CoNLL-U ...
7
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
... Dat Quoc Nguyen, Mark Dras, and Mark Johnson. 2017. A novel neural network model for joint POS tagging and graph-based dependency parsing. In Proceedings of the CoNLL 2017 Shared Task: Mul- tilingual Parsing from ...
19
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
... from Raw Text to Universal Dependencies, and two overview papers: one summarizing the main task, its features, evaluation methodology for the main and additional metrics, and some interesting observations ...
8
CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
... Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 1–21 Brussels, Belgium, October 31 – November 1, 2018.. c©2018 Association [r] ...
21
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
... from Raw Text to Universal Dependencies and an overview paper summarizing the task, its features, evaluation methodology for the main and additional metrics, and some interesting observations about the ...
12
Training Data Augmentation for Context Sensitive Neural Lemmatizer Using Inflection Tables and Raw Text
... Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches ...
10
Discriminative Boosting from Dictionary and Raw Text – A Novel Approach to Build A Chinese Word Segmenter
... Chinese word segmentation (CWS) is a basic and important task for Chinese information processing. Standard approaches to CWS treat it as a sequence labelling task. Without manually annotated corpora, these approaches are ...
10
NLP Cube: End to End Raw Text Processing With Neural Networks
... We introduce NLP-Cube: an end-to-end Natural Language Processing framework, evaluated in CoNLL’s “Multilingual Par- sing from Raw Text to Universal Depen- dencies 2018” Shared Task. It performs sentence ...
9
Language Resource Addition Strategies for Raw Text Parsing
... of raw text parsing, from the viewpoint of language resource ...the raw text parsing is divided into three steps: word segmentation, part-of-speech tagging, and dependency ...
8
Cross Lingual Transfer of Semantic Roles: From Raw Text to Semantic Roles
... We have described a method for cross-lingual transfer of dependency-based SRL systems via annotation projection. Our model is agnostic to linguistic features leading to a robust model that can be trained on projected ...
11
Corpus Selection Approaches for Multilingual Parsing from Raw Text to Universal Dependencies
... The methodol- ogy is simple: We use similarity mea- sures to select a corpus from available training data even from multiple corpora for surprise languages and use the re- sulting corpus[r] ...
9
Subcategorisation Acquisition from Raw Text for a Free Word Order Language
... newspaper text; the trained parser model contained explicit subcategorisation frequencies, which could then be extracted to construct a subcategorisation lexicon for 14,229 German ...
10
Learning to Embed Semantic Correspondence for Natural Language Understanding
... The goal of NLU is to extract meaning from a nat- ural language and infer the user intention. NLU typically involves two tasks: identifying user in- tent and extracting domain-specific entities, the second of which is ...
10
Large Coverage Root Lexicon Extraction for Hindi
... Name POS Paradigm Suffixes Root laDkA noun {‘A’,‘e’,‘on’} ‘A’ laDkI noun {‘I’,‘iyAn’} ‘I’ dho verb {‘’,‘yogI’,‘nA’,. . . } ‘’ chal verb {‘’,‘ogI’,‘nA’,. . . } ‘’ Table 3: Sample Paradigm Suffix Sets Since Hindi word ...
9
Neural Semi Markov Conditional Random Fields for Robust Character Based Part of Speech Tagging
... Anders Bj¨orkelund, Agnieszka Falenska, Xiang Yu, and Jonas Kuhn. 2017. IMS at the CoNLL 2017 UD shared task: CRFs and perceptrons meet neu- ral networks. In Proceedings of the CoNLL 2017 Shared Task: Multilingual ...
8
Morphological Word Embeddings
... We augment the log-bilinear model (LBL) of Mnih and Hinton (2007) with a multi-task objective. In addition to raw text, our model is trained on a corpus annotated with morphological tags, encour- aging the ...
6
A Heuristic Approach to Factoid Question Generation from Sentence
... or text format which is then further processed using a pre-processing algorithm which converts the raw text into a structured format we generate a syntactical parsetree for each sentence in a corpus ...
9
Impact of MWE Resources on Multiword Recognition
... In this paper, we demonstrate the impact of Multiword Expression (MWE) resources in the task of MWE recognition in text. We present results based on the Wiki50 cor- pus for MWE resources, generated using ...
5