Detecting Multiword Expressions and Named Entities in Natural Language Texts

Technology is becoming expressively popular among agribusiness producers and is progressing in all agricultural area. One of the difficulties in this context is to handle data in natural language to solve problems in the field of agriculture. In order to build up dialogs and provide rich researchers, the present work uses Natural Language Processing (NLP) techniques to develop an automatic and effective computer system to interact with the user and assist in the identification of pests and diseases in the soybean farming, stored in a database repository to provide accurate diagnoses to simplify the work of the agricultural professional and also for those who deal with a lot of information in this area. Information on 108 pests and 19 diseases that damage Brazilian soybean was collected from Brazilian bibliographic manuals with the purpose to optimize the data and improve production, using the spaCy library for syntactic analysis of NLP, which allowed the pre-process the texts, recognize the named entities, calculate the similarity between the words, verify dependency parsing and also provided the support for the development requirements of the CAROLINA tool (Robotized Agronomic Conversation in Natural Language) using the language belonging to the agricultural area.

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SimpleApprenant: a platform to improve French L2 learners’ knowledge of multiword expressions

CALL and complexity – short papers from EUROCALL 2019 ◽

10.14705/rpnet.2019.38.1036 ◽

2019 ◽

pp. 356-361

Author(s):

Amalia Todirascu ◽

Marion Cargill

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Preliminary Evaluation ◽

Multiword Expressions ◽

Transformation Rules ◽

L2 Learners ◽

The Common ◽

Writing Exercises ◽

Spelling Checker

We present SimpleApprenant, a platform aiming to improve French L2 learners’ knowledge of Multi Word Expressions (MWEs). SimpleApprenant integrates an MWE database annotated with the Common European Framework of Reference for languages (CEFR) level and several Natural Language Processing (NLP) tools: a spelling checker, a parser, and a set of transformation rules. NLP tools and resources are used to build training and writing exercises to improve MWE knowledge and writing skills of French L2 learners. We present the user scenarios, the platform’s architecture, as well as the preliminary evaluation of its NLP tools.

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Discovering multiword expressions

Natural Language Engineering ◽

10.1017/s1351324919000494 ◽

2019 ◽

Vol 25 (06) ◽

pp. 715-733

Author(s):

Aline Villavicencio ◽

Marco Idiart

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Canonical Forms ◽

Multiword Expressions

AbstractIn this paper, we provide an overview of research on multiword expressions (MWEs), from a natural language processing perspective. We examine methods developed for modelling MWEs that capture some of their linguistic properties, discussing their use for MWE discovery and for idiomaticity detection. We concentrate on their collocational and contextual preferences, along with their fixedness in terms of canonical forms and their lack of word-for-word translatatibility. We also discuss a sample of the MWE resources that have been used in intrinsic evaluation setups for these methods.

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Disambiguating the species of biomedical named entities using natural language parsers

Bioinformatics ◽

10.1093/bioinformatics/btq002 ◽

2010 ◽

Vol 26 (5) ◽

pp. 661-667 ◽

Cited By ~ 40

Author(s):

X. Wang ◽

J. Tsujii ◽

S. Ananiadou

Keyword(s):

Natural Language ◽

Named Entities

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Automated Trait Extraction using ClearEarth, a Natural Language Processing System for Text Mining in Natural Sciences

Biodiversity Information Science and Standards ◽

10.3897/biss.2.26080 ◽

2018 ◽

Vol 2 ◽

pp. e26080 ◽

Cited By ~ 1

Author(s):

Anne Thessen ◽

Jenette Preciado ◽

Payoj Jain ◽

James Martin ◽

Martha Palmer ◽

...

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Processing System ◽

Natural Sciences ◽

Phenotypic Traits ◽

Good Precision ◽

Named Entities ◽

Clinical Notes ◽

Linguistic Annotation

The cTAKES package (using the ClearTK Natural Language Processing toolkit Bethard et al. 2014,http://cleartk.github.io/cleartk/) has been successfully used to automatically read clinical notes in the medical field (Albright et al. 2013, Styler et al. 2014). It is used on a daily basis to automatically process clinical notes and extract relevant information by dozens of medical institutions. ClearEarth is a collaborative project that brings together computational linguistics and domain scientists to port Natural Language Processing (NLP) modules trained on the same types of linguistic annotation to the fields of geology, cryology, and ecology. The goal for ClearEarth in the ecology domain is the extraction of ecologically-relevant terms, including eco-phenotypic traits from text and the assignment of those traits to taxa. Four annotators used Anafora (an annotation software; https://github.com/weitechen/anafora) to mark seven entity types (biotic, aggregate, abiotic, locality, quality, unit, value) and six reciprocal property types (synonym of/has synonym, part of/has part, subtype/supertype) in 133 documents from primarily Encyclopedia of Life (EOL) and Wikipedia according to project guidelines (https://github.com/ClearEarthProject/AnnotationGuidelines). Inter-annotator agreement ranged from 43% to 90%. Performance of ClearEarth on identifying named entities in biology text overall was good (precision: 85.56%; recall: 71.57%). The named entities with the best performance were organisms and their parts/products (biotic entities - precision: 72.09%; recall: 54.17%) and systems and environments (aggregate entities - precision: 79.23%; recall: 75.34%). Terms and their relationships extracted by ClearEarth can be embedded in the new ecocore ontology after vetting (http://www.obofoundry.org/ontology/ecocore.html). This project enables use of advanced industry and research software within natural sciences for downstream operations such as data discovery, assessment, and analysis. In addition, ClearEarth uses the NLP results to generate domain-specific ontologies and other semantic resources.

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Identification of Multiword Expressions by Combining Multiple Linguistic Information Sources

Computational Linguistics ◽

10.1162/coli_a_00177 ◽

2014 ◽

Vol 40 (2) ◽

pp. 449-468 ◽

Cited By ~ 6

Author(s):

Yulia Tsvetkov ◽

Shuly Wintner

Keyword(s):

Natural Language ◽

Bayesian Network ◽

Information Sources ◽

Identification Accuracy ◽

Multiple Sources ◽

Language Resources ◽

Linguistic Information ◽

Multiword Expressions ◽

Text Corpora

We propose a framework for using multiple sources of linguistic information in the task of identifying multiword expressions in natural language texts. We define various linguistically motivated classification features and introduce novel ways for computing them. We then manually define interrelationships among the features, and express them in a Bayesian network. The result is a powerful classifier that can identify multiword expressions of various types and multiple syntactic constructions in text corpora. Our methodology is unsupervised and language-independent; it requires relatively few language resources and is thus suitable for a large number of languages. We report results on English, French, and Hebrew, and demonstrate a significant improvement in identification accuracy, compared with less sophisticated baselines.

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Computational Treatment of Multiword Expressions

The Oxford Handbook of Computational Linguistics 2nd edition ◽

10.1093/oxfordhb/9780199573691.013.56 ◽

2018 ◽

Author(s):

Carlos Ramisch ◽

Aline Villavicencio

Keyword(s):

Natural Language ◽

Language Processing ◽

Word Sense Disambiguation ◽

Word Sense ◽

Language Generation ◽

Multiword Expressions ◽

Language Technology ◽

Sense Disambiguation ◽

Technology Applications ◽

Nominal Compounds

In natural-language processing, multiword expressions (MWEs) have been the focus of much attention in their many forms, including idioms, nominal compounds, verbal expressions, and collocations. In addition to their relevance for lexicographic and terminographic work, their ubiquity in language affects the performance of tasks like parsing, word sense disambiguation, and natural-language generation. They lend a mark of naturalness and fluency to applications that can deal with them, ranging from machine translation to information retrieval. This chapter presents an overview of their linguistic characteristics and discusses a variety of proposals for incorporating them into language technology, covering type-based discovery, token-based identification, and MWE-aware language technology applications.

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Natural language processing systems for data extraction and mapping on the basis of unstructured text blocks

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/2414-9179-2020-1-26-375-384 ◽

2020 ◽

Vol 26 (1) ◽

pp. 375-384

Author(s):

Alexey Kolesnikov ◽

Pavel Kikin ◽

Giovanni Niko ◽

Elena Komissarova

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Data Extraction ◽

Named Entity Recognition ◽

Point Of View ◽

Entity Recognition ◽

Named Entities ◽

Processing Technologies ◽

Plain Text

Modern natural language processing technologies allow you to work with texts without being a specialist in linguistics. The use of popular data processing platforms for the development and use of linguistic models provides an opportunity to implement them in popular geographic information systems. This feature allows you to significantly expand the functionality and improve the accuracy of standard geocoding functions. The article provides a comparison of the most popular methods and software implemented on their basis, using the example of solving the problem of extracting geographical names from plain text. This option is an extended version of the geocoding operation, since the result also includes the coordinates of the point features of interest, but there is no need to separately extract the addresses or geographical names of the objects in advance from the text. In computer linguistics, this problem is solved by the methods of extracting named entities (Eng. named entity recognition). Among the most modern approaches to the final implementation, the authors of the article have chosen algorithms based on rules, models of maximum entropy and convolutional neural networks. The selected algorithms and methods were evaluated not only from the point of view of the accuracy of searching for geographical objects in the text, but also from the point of view of simplicity of refinement of the basic rules or mathematical models using their own text bodies. Reports on technological violations, accidents and incidents at the facilities of the heat and power complex of the Ministry of Energy of the Russian Federation were selected as the initial data for testing the abovementioned methods and software solutions. Also, a study is presented on a method for improving the quality of recognition of named entities based on additional training of a neural network model using a specialized text corpus.

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A French corpus annotated for multiword expressions and named entities

Journal of Language Modelling ◽

10.15398/jlm.v8i2.265 ◽

2021 ◽

Vol 8 (2) ◽

Author(s):

Marie Candito ◽

Mathieu Constant ◽

Carlos Ramisch ◽

Agata Savary ◽

Bruno Guillaume ◽

...

Keyword(s):

Sufficient Criterion ◽

Named Entities ◽

Multiword Expressions ◽

Clear Cut ◽

Lexical Substitution ◽

Cross Type ◽

Person Location ◽

French Corpus

We present the enrichment of a French treebank of various genres with a new annotation layer for multiword expressions (MWEs) and named entities (NEs).1 Our contribution with respect to previous work on NE and MWE annotation is the particular care taken to use formal criteria, organized into decision flowcharts, shedding some light on the interactions between NEs and MWEs. Moreover, in order to cope with the well-known difficulty to draw a clear-cut frontier between compositional expressions and MWEs, we chose to use sufficient criteria only. As a result, annotated MWEs satisfy a varying number of sufficient criteria, accounting for the scalar nature of the MWE status. In addition to the span of the elements, annotation includes the subcategory of NEs (e.g., person, location) and one matching sufficient criterion for non-verbal MWEs (e.g., lexical substitution). The 3,099 sentences of the treebank were double-annotated and adjudicated, and we paid attention to cross-type consistency and compatibility with thesyntactic layer. Overall inter-annotator agreement on non-verbal MWEs and NEs reached 71.1%. The released corpus contains 3,112 annotated NEs and 3,440 MWEs, and is distributed under an open license.

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