Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance

Mixing and blending syntactic and semantic dependencies

Our system for the CoNLL 2008 shared task uses a set of individual parsers, a set of stand-alone semantic role labellers, and a joint system for parsing and semantic role labelling, all blended together. The system achieved a macro averaged labelled F1- score of 79.79 (WSJ 80.92, Brown 70.49) for the overall task. The labelled attachment score for syntactic dependencies was 86.63 (WSJ 87.36, Brown 80.77) and the labelled F1-score for semantic dependencies was 72.94 (WSJ 74.47, Brown 60.18)

Japanese word prediction

This report deals with the implementation of a Japanese word prediction engine written by the author. As this type of software does not seem to exist for Japanese at the time of writing, it could prove useful in Japanese augmentative and alternative communication (AAC) as a software tool used to improve typing speed and reduce the amount of keystrokes needed to produce text. Word prediction, in contrast to the word completion software commonly found in mobile phones and word processor intellisense engines etc. is a technique for suggesting a followup word after a word has just been completed. This is usually done by providing a list of the most probable words to the user, sorted by commonality (general and user-specific frequency). Combined with good word completion software and a responsive user interface, word prediction is one of the most powerful assistive tools available to movement impaired users today. The main goals of the thesis will be to: 1. Answer as many of the questions raised by the language differences as possible. 2. Investigate further avenues of research in the subject. 3. Make a functional word prediction prototype for Japanese. All project code is in the public domain and is currently hosted at: http://www.mediafire.com/?rrhqtqsgp6ei6m

RobertNLP at the IWPT 2021 shared task: simple enhanced UD parsing for 17 languages

This paper presents our multilingual dependency parsing system as used in the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies. Our system consists of an unfactorized biaffine classifier that operates directly on fine-tuned XLM-R embeddings and generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens. To avoid sparsity issues resulting from lexicalized dependency labels, we replace lexical items in relations with placeholders at training and prediction time, later retrieving them from the parse via a hybrid rule-based/machine-learning system. In addition, we utilize model ensembling at prediction time. Our system achieves high parsing accuracy on the blind test data, ranking 3rd out of 9 with an average ELAS F1 score of 86.97

Automatic Variation of Swedish Text by Syntactic Fronting

Proceedings of the Workshop on NLP for Reading and Writing – Resources, Algorithms and Tools (SLTC 2008). Editors: Rickard Domeij, Sofie Johansson Kokkinakis, Ola Knutsson and Sylvana Sofkova Hashemi. NEALT Proceedings Series, Vol. 3 (2009), 22-23. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/4116

Extracting Formal Models from Normative Texts

We are concerned with the analysis of normative texts - documents based on the deontic notions of obligation, permission, and prohibition. Our goal is to make queries about these notions and verify that a text satisfies certain properties concerning causality of actions and timing constraints. This requires taking the original text and building a representation (model) of it in a formal language, in our case the C-O Diagram formalism. We present an experimental, semi-automatic aid that helps to bridge the gap between a normative text in natural language and its C-O Diagram representation. Our approach consists of using dependency structures obtained from the state-of-the-art Stanford Parser, and applying our own rules and heuristics in order to extract the relevant components. The result is a tabular data structure where each sentence is split into suitable fields, which can then be converted into a C-O Diagram. The process is not fully automatic however, and some post-editing is generally required of the user. We apply our tool and perform experiments on documents from different domains, and report an initial evaluation of the accuracy and feasibility of our approach.Comment: Extended version of conference paper at the 21st International Conference on Applications of Natural Language to Information Systems (NLDB 2016). arXiv admin note: substantial text overlap with arXiv:1607.0148