scholarly journals The Value of Paraphrase for Knowledge Base Predicates

2020 ◽  
Vol 34 (05) ◽  
pp. 9346-9353
Author(s):  
Bingcong Xue ◽  
Sen Hu ◽  
Lei Zou ◽  
Jiashu Cheng
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
Knowledge Bases ◽  
Human Beings ◽  
High Quality ◽  
Language Generation

Paraphrase, i.e., differing textual realizations of the same meaning, has proven useful for many natural language processing (NLP) applications. Collecting paraphrase for predicates in knowledge bases (KBs) is the key to comprehend the RDF triples in KBs. Existing works have published some paraphrase datasets automatically extracted from large corpora, but have too many redundant pairs or don't cover enough predicates, which cannot be improved by computer only and need the help of human beings. This paper shows a full process of collecting large-scale and high-quality paraphrase dictionaries for predicates in knowledge bases, which takes advantage of existing datasets and combines the technologies of machine mining and crowdsourcing. Our dataset comprises 2284 distinct predicates in DBpedia and 31130 paraphrase pairs in total, the quality of which is a great leap over previous works. Then it is demonstrated that such good paraphrase dictionaries can do great help to natural language processing tasks such as question answering and language generation. We also publish our own dictionary for further research.

scholarly journals Natural Language Processing: Components, Advances, Tools and Industrial Applications

2021 ◽  
Vol 9 (9) ◽  
pp. 964-971
Author(s):  
Hima Yeldo
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Question Answering ◽  
Industrial Applications ◽  
Spam Detection ◽  
Language Understanding ◽  
Language Generation ◽  
Email Spam

Abstract: Natural Language Processing is the study that focuses the interplay between computer and the human languages NLP has spread its applications in various fields such as an email Spam detection, machine translation, summation, information extraction, and question answering etc. Natural Language Processing classifies two parts i.e. Natural Language Generation and Natural Language understanding which evolves the task to generate and understand the text.

scholarly journals Special Thematic Section on Semantic Models for Natural Language Processing (Preface)

2018 ◽  
Vol 18 (1) ◽  
pp. 93-94
Author(s):  
Kiril Simov ◽  
Petya Osenova
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Open Data ◽  
Knowledge Bases ◽  
Lexical Resources ◽  
Text Simplification ◽  
Semantic Models ◽  
Semantic Resources

Abstract With the availability of large language data online, cross-linked lexical resources (such as BabelNet, Predicate Matrix and UBY) and semantically annotated corpora (SemCor, OntoNotes, etc.), more and more applications in Natural Language Processing (NLP) have started to exploit various semantic models. The semantic models have been created on the base of LSA, clustering, word embeddings, deep learning, neural networks, etc., and abstract logical forms, such as Minimal Recursion Semantics (MRS) or Abstract Meaning Representation (AMR), etc. Additionally, the Linguistic Linked Open Data Cloud has been initiated (LLOD Cloud) which interlinks linguistic data for improving the tasks of NLP. This cloud has been expanding enormously for the last four-five years. It includes corpora, lexicons, thesauri, knowledge bases of various kinds, organized around appropriate ontologies, such as LEMON. The semantic models behind the data organization as well as the representation of the semantic resources themselves are a challenge to the NLP community. The NLP applications that extensively rely on the above discussed models include Machine Translation, Information Extraction, Question Answering, Text Simplification, etc.

scholarly journals Improving Natural Language Inference Using External Knowledge in the Science Questions Domain

2019 ◽  
Vol 33 ◽  
pp. 7208-7215 ◽  
Author(s):  
Xiaoyan Wang ◽  
Pavan Kapanipathi ◽  
Ryan Musa ◽  
Mo Yu ◽  
Kartik Talamadupula ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Improve Performance ◽  
External Knowledge ◽  
Structured Knowledge ◽  
Significant Attention

Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained significant attention due to the release of large scale, challenging datasets. Present approaches to the problem largely focus on learning-based methods that use only textual information in order to classify whether a given premise entails, contradicts, or is neutral with respect to a given hypothesis. Surprisingly, the use of methods based on structured knowledge – a central topic in artificial intelligence – has not received much attention vis-a-vis the NLI problem. While there are many open knowledge bases that contain various types of reasoning information, their use for NLI has not been well explored. To address this, we present a combination of techniques that harness external knowledge to improve performance on the NLI problem in the science questions domain. We present the results of applying our techniques on text, graph, and text-and-graph based models; and discuss the implications of using external knowledge to solve the NLI problem. Our model achieves close to state-of-the-art performance for NLI on the SciTail science questions dataset.

Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

2021 ◽  
Author(s):  
Xinxu Shen ◽  
Troy Houser ◽  
David Victor Smith ◽  
Vishnu P. Murty
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Individual Difference ◽  
Language Processing ◽  
Large Scale ◽  
High Reliability ◽  
Difference Analysis ◽  
Universal Sentence ◽  
Natural Language Processing Tool

The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recall. We compared the reliability in scoring made between two independent raters (i.e., hand-scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique, video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand-scoring, and further that the results using USE outperformed another popular natural language processing tool, GloVe. In study two, we tested whether our automated approach remained valid when testing individual’s varying on clinically-relevant dimensions that influence episodic memory, age and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approaches implementing USE are a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.

A survey of question answering in natural language processing

Poetics ◽  
1990 ◽  
Vol 19 (1-2) ◽  
pp. 99-120
Author(s):  
Stefan Wermter ◽  
Wendy G. Lehnert
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering

scholarly journals Designing and Validating an Annotation Model of Dynamic Modality for English and Spanish: Issues and Problems

10.29007/pc58 ◽  
2018 ◽  
Author(s):  
Julia Lavid ◽  
Marta Carretero ◽  
Juan Rafael Zamorano
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Reliability Study ◽  
Annotation Scheme ◽  
High Degree ◽  
Difficult Cases

In this paper we set forth an annotation model for dynamic modality in English and Spanish, given its relevance not only for contrastive linguistic purposes, but also for its impact on practical annotation tasks in the Natural Language Processing (NLP) community. An annotation scheme is proposed, which captures both the functional-semantic meanings and the language-specific realisations of dynamic meanings in both languages. The scheme is validated through a reliability study performed on a randomly selected set of one hundred and twenty sentences from the MULTINOT corpus, resulting in a high degree of inter-annotator agreement. We discuss our main findings and give attention to the difficult cases as they are currently being used to develop detailed guidelines for the large-scale annotation of dynamic modality in English and Spanish.

scholarly journals Comparison of Templates with Word2vec in Finding Semantic Relations Between Words

2018 ◽  
pp. 13-17
Author(s):  
Kaan Ant ◽  
Ugur Sogukpinar ◽  
Mehmet Fatif Amasyali
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Semantic Relations ◽  
Template Method ◽  
Semantic Relationships ◽  
Semantic Spaces

The use of databases those containing semantic relationships between words is becoming increasingly widespread in order to make natural language processing work more effective. Instead of the word-bag approach, the suggested semantic spaces give the distances between words, but they do not express the relation types. In this study, it is shown how semantic spaces can be used to find the type of relationship and it is compared with the template method. According to the results obtained on a very large scale, while is_a and opposite are more successful for semantic spaces for relations, the approach of templates is more successful in the relation types at_location, made_of and non relational.

scholarly journals Enhancing Natural Language Inference Using New and Expanded Training Data Sets and New Learning Models

2020 ◽  
Vol 34 (05) ◽  
pp. 8504-8511
Author(s):  
Arindam Mitra ◽  
Ishan Shrivastava ◽  
Chitta Baral
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Training Data ◽  
Data Sets ◽  
Learning Models ◽  
New Learning ◽  
Word Attention ◽  
Attention Function

Natural Language Inference (NLI) plays an important role in many natural language processing tasks such as question answering. However, existing NLI modules that are trained on existing NLI datasets have several drawbacks. For example, they do not capture the notion of entity and role well and often end up making mistakes such as “Peter signed a deal” can be inferred from “John signed a deal”. As part of this work, we have developed two datasets that help mitigate such issues and make the systems better at understanding the notion of “entities” and “roles”. After training the existing models on the new dataset we observe that the existing models do not perform well on one of the new benchmark. We then propose a modification to the “word-to-word” attention function which has been uniformly reused across several popular NLI architectures. The resulting models perform as well as their unmodified counterparts on the existing benchmarks and perform significantly well on the new benchmarks that emphasize “roles” and “entities”.

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