scholarly journals Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings

2018 ◽  
Author(s):  
Debanjan Mahata ◽  
John Kuriakose ◽  
Rajiv Ratn Shah ◽  
Roger Zimmermann
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Keyphrase Extraction ◽  
Text Documents ◽  
Benchmark Datasets

Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.

scholarly journals Attention as Relation: Learning Supervised Multi-head Self-Attention for Relation Extraction

2020 ◽  
Author(s):  
Jie Liu ◽  
Shaowei Chen ◽  
Bingquan Wang ◽  
Jiaxin Zhang ◽  
Na Li ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Relation Extraction ◽  
Attention Mechanism ◽  
Entity Extraction ◽  
Relation Type ◽  
Benchmark Datasets ◽  
Relation Learning

Joint entity and relation extraction is critical for many natural language processing (NLP) tasks, which has attracted increasing research interest. However, it is still faced with the challenges of identifying the overlapping relation triplets along with the entire entity boundary and detecting the multi-type relations. In this paper, we propose an attention-based joint model, which mainly contains an entity extraction module and a relation detection module, to address the challenges. The key of our model is devising a supervised multi-head self-attention mechanism as the relation detection module to learn the token-level correlation for each relation type separately. With the attention mechanism, our model can effectively identify overlapping relations and flexibly predict the relation type with its corresponding intensity. To verify the effectiveness of our model, we conduct comprehensive experiments on two benchmark datasets. The experimental results demonstrate that our model achieves state-of-the-art performances.

scholarly journals Drop Redundant, Shrink Irrelevant: Selective Knowledge Injection for Language Pretraining

2021 ◽  
Author(s):  
Ningyu Zhang ◽  
Shumin Deng ◽  
Xu Cheng ◽  
Xi Chen ◽  
Yichi Zhang ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Prior Knowledge ◽  
Language Processing ◽  
Empirical Investigation ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Experimental Results ◽  
Benchmark Datasets ◽  
Injection Methods

Previous research has demonstrated the power of leveraging prior knowledge to improve the performance of deep models in natural language processing. However, traditional methods neglect the fact that redundant and irrelevant knowledge exists in external knowledge bases. In this study, we launched an in-depth empirical investigation into downstream tasks and found that knowledge-enhanced approaches do not always exhibit satisfactory improvements. To this end, we investigate the fundamental reasons for ineffective knowledge infusion and present selective injection for language pretraining, which constitutes a model-agnostic method and is readily pluggable into previous approaches. Experimental results on benchmark datasets demonstrate that our approach can enhance state-of-the-art knowledge injection methods.

Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

2019 ◽  
Vol 53 (2) ◽  
pp. 3-10
Author(s):  
Muthu Kumar Chandrasekaran ◽  
Philipp Mayr
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Digital Libraries ◽  
State Of The Art ◽  
Shared Task ◽  
Processing Information ◽  
Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

LIS4: Lesk Inspired Sense Specific Semantic Similarity using WordNet

2021 ◽  
pp. 2150006
Author(s):  
Saravanakumar Kandasamy ◽  
Aswani Kumar Cherukuri
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Gold Standard ◽  
Question Answering ◽  
Knowledge Based ◽  
Benchmark Datasets ◽  
Processing Information

Semantic similarity quantification between concepts is one of the inevitable parts in domains like Natural Language Processing, Information Retrieval, Question Answering, etc. to understand the text and their relationships better. Last few decades, many measures have been proposed by incorporating various corpus-based and knowledge-based resources. WordNet and Wikipedia are two of the Knowledge-based resources. The contribution of WordNet in the above said domain is enormous due to its richness in defining a word and all of its relationship with others. In this paper, we proposed an approach to quantify the similarity between concepts that exploits the synsets and the gloss definitions of different concepts using WordNet. Our method considers the gloss definitions, contextual words that are helping in defining a word, synsets of contextual word and the confidence of occurrence of a word in other word’s definition for calculating the similarity. The evaluation based on different gold standard benchmark datasets shows the efficiency of our system in comparison with other existing taxonomical and definitional measures.

News Headline Building using Hybrid Headline Generation Technique for Quick Gist

2017 ◽  
Vol 6 (1) ◽  
pp. 36-52
Author(s):  
Urmila Shrawankar ◽  
Kranti Wankhede
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Reading Time ◽  
News Article ◽  
Keyword Extraction ◽  
Keyphrase Extraction ◽  
Generation Technique ◽  
Extraction Algorithm ◽  
Key Terms

A considerable amount of time is required to interpret whole news article to get the gist of it. Therefore, in order to reduce the reading and interpretation time, headlines are necessary. The available techniques for news headline construction mainly includes extractive and abstractive headline generation techniques. In this paper, context based news headline is formed from long news article by using techniques of core Natural Language Processing (NLP) and key terms of news article. Key terms are retrieved from lengthy news article by using various approaches of keyword extraction. The keyphrases are picked out using Keyphrase Extraction Algorithm (KEA) which helps to construct headline syntax along with NLP's parsing technique. Sentence compression algorithm helps to generate compressed sentences from generated parse tree of leading sentences. Headline helps user for reducing cognitive burden of reader by reflecting important contents of news. The objective is to frame headline using key terms for reducing reading time and efforts of reader.

Textual entailment graphs

2015 ◽  
Vol 21 (5) ◽  
pp. 699-724 ◽  
Author(s):  
LILI KOTLERMAN ◽  
IDO DAGAN ◽  
BERNARDO MAGNINI ◽  
LUISA BENTIVOGLI
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Gold Standard ◽  
State Of The Art ◽  
Text Analytics ◽  
Joint Work ◽  
Gold Standard Dataset ◽  
Textual Entailment ◽  
Interesting Task

AbstractIn this work, we present a novel type of graphs for natural language processing (NLP), namely textual entailment graphs (TEGs). We describe the complete methodology we developed for the construction of such graphs and provide some baselines for this task by evaluating relevant state-of-the-art technology. We situate our research in the context of text exploration, since it was motivated by joint work with industrial partners in the text analytics area. Accordingly, we present our motivating scenario and the first gold-standard dataset of TEGs. However, while our own motivation and the dataset focus on the text exploration setting, we suggest that TEGs can have different usages and suggest that automatic creation of such graphs is an interesting task for the community.

scholarly journals Unsupervised Keyphrase Extraction for Web Pages

2019 ◽  
Vol 3 (3) ◽  
pp. 58 ◽  
Author(s):  
Tim Haarman ◽  
Bastiaan Zijlema ◽  
Marco Wiering
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Structural Information ◽  
Extraction Methods ◽  
Web Pages ◽  
Keyphrase Extraction ◽  
Text Documents ◽  
Normal Text ◽  
Textual Data ◽  
Novel Method

Keyphrase extraction is an important part of natural language processing (NLP) research, although little research is done in the domain of web pages. The World Wide Web contains billions of pages that are potentially interesting for various NLP tasks, yet it remains largely untouched in scientific research. Current research is often only applied to clean corpora such as abstracts and articles from academic journals or sets of scraped texts from a single domain. However, textual data from web pages differ from normal text documents, as it is structured using HTML elements and often consists of many small fragments. These elements are furthermore used in a highly inconsistent manner and are likely to contain noise. We evaluated the keyphrases extracted by several state-of-the-art extraction methods and found that they did not transfer well to web pages. We therefore propose WebEmbedRank, an adaptation of a recently proposed extraction method that can make use of structural information in web pages in a robust manner. We compared this novel method to other baselines and state-of-the-art methods using a manually annotated dataset and found that WebEmbedRank achieved significant improvements over existing extraction methods on web pages.

scholarly journals Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

scholarly journals The state of the art in text mining and natural language processing for pharmacogenomics

2012 ◽  
Vol 45 (5) ◽  
pp. 825-826 ◽  
Author(s):  
Adrien Coulet ◽  
K. Bretonnel Cohen ◽  
Russ B. Altman
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
The State

scholarly journals Biomedical Concept Recognition Using Deep Neural Sequence Models

2019 ◽  
Author(s):  
Negacy D. Hailu ◽  
Michael Bada ◽  
Asmelash Teka Hadgu ◽  
Lawrence E. Hunter
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Concept Recognition ◽  
Performance Improvements ◽  
Art Performance

AbstractBackgroundthe automated identification of mentions of ontological concepts in natural language texts is a central task in biomedical information extraction. Despite more than a decade of effort, performance in this task remains below the level necessary for many applications.Resultsrecently, applications of deep learning in natural language processing have demonstrated striking improvements over previously state-of-the-art performance in many related natural language processing tasks. Here we demonstrate similarly striking performance improvements in recognizing biomedical ontology concepts in full text journal articles using deep learning techniques originally developed for machine translation. For example, our best performing system improves the performance of the previous state-of-the-art in recognizing terms in the Gene Ontology Biological Process hierarchy, from a previous best F1 score of 0.40 to an F1 of 0.70, nearly halving the error rate. Nearly all other ontologies show similar performance improvements.ConclusionsA two-stage concept recognition system, which is a conditional random field model for span detection followed by a deep neural sequence model for normalization, improves the state-of-the-art performance for biomedical concept recognition. Treating the biomedical concept normalization task as a sequence-to-sequence mapping task similar to neural machine translation improves performance.

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