Domain specific question answering technique for accessing information on mobile phone

2008 ◽  
Author(s):  
A.J. Agrawal
Keyword(s):  
Mobile Phone ◽  
Question Answering ◽  
Specific Question ◽  
Domain Specific

scholarly journals What Can We Learn from Almost a Decade of Food Tweets

2020 ◽  
Author(s):  
Uga Sproģis ◽  
Matīss Rikters
Keyword(s):  
Sentiment Analysis ◽  
Question Answering ◽  
Time Span ◽  
Use Cases ◽  
Specific Question ◽  
Domain Specific ◽  
Question And Answer ◽  
Analysis Models ◽  
Over Time

We present the Latvian Twitter Eater Corpus - a set of tweets in the narrow domain related to food, drinks, eating and drinking. The corpus has been collected over time-span of over 8 years and includes over 2 million tweets entailed with additional useful data. We also separate two sub-corpora of question and answer tweets and sentiment annotated tweets. We analyse the contents of the corpus and demonstrate use-cases for the sub-corpora by training domain-specific question-answering and sentiment-analysis models using the data from the corpus.

Geoscience Language Processing for Exploration

2021 ◽  
Author(s):  
Huseyin Denli ◽  
Hassan A Chughtai ◽  
Brian Hughes ◽  
Robert Gistri ◽  
Peng Xu
Keyword(s):  
Language Processing ◽  
Similarity Search ◽  
Question Answering ◽  
Language Translation ◽  
Automated Analysis ◽  
General Purpose ◽  
Step Change ◽  
Domain Specific ◽  
Specific Meaning ◽  
Processing Solution

Abstract Deep learning has recently been providing step-change capabilities, particularly using transformer models, for natural language processing applications such as question answering, query-based summarization, and language translation for general-purpose context. We have developed a geoscience-specific language processing solution using such models to enable geoscientists to perform rapid, fully-quantitative and automated analysis of large corpuses of data and gain insights. One of the key transformer-based model is BERT (Bidirectional Encoder Representations from Transformers). It is trained with a large amount of general-purpose text (e.g., Common Crawl). Use of such a model for geoscience applications can face a number of challenges. One is due to the insignificant presence of geoscience-specific vocabulary in general-purpose context (e.g. daily language) and the other one is due to the geoscience jargon (domain-specific meaning of words). For example, salt is more likely to be associated with table salt within a daily language but it is used as a subsurface entity within geosciences. To elevate such challenges, we retrained a pre-trained BERT model with our 20M internal geoscientific records. We will refer the retrained model as GeoBERT. We fine-tuned the GeoBERT model for a number of tasks including geoscience question answering and query-based summarization. BERT models are very large in size. For example, BERT-Large has 340M trained parameters. Geoscience language processing with these models, including GeoBERT, could result in a substantial latency when all database is processed at every call of the model. To address this challenge, we developed a retriever-reader engine consisting of an embedding-based similarity search as a context retrieval step, which helps the solution to narrow the context for a given query before processing the context with GeoBERT. We built a solution integrating context-retrieval and GeoBERT models. Benchmarks show that it is effective to help geologists to identify answers and context for given questions. The prototype will also produce a summary to different granularity for a given set of documents. We have also demonstrated that domain-specific GeoBERT outperforms general-purpose BERT for geoscience applications.

scholarly journals TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection

2020 ◽  
Vol 34 (05) ◽  
pp. 7780-7788
Author(s):  
Siddhant Garg ◽  
Thuy Vu ◽  
Alessandro Moschitti
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Positive Impact ◽  
Fine Tuning ◽  
Target Domain ◽  
Domain Specific ◽  
Transfer Step ◽  
Industrial Setting ◽  
Large Scale Dataset ◽  
Effective Use

We propose TandA, an effective technique for fine-tuning pre-trained Transformer models for natural language tasks. Specifically, we first transfer a pre-trained model into a model for a general task by fine-tuning it with a large and high-quality dataset. We then perform a second fine-tuning step to adapt the transferred model to the target domain. We demonstrate the benefits of our approach for answer sentence selection, which is a well-known inference task in Question Answering. We built a large scale dataset to enable the transfer step, exploiting the Natural Questions dataset. Our approach establishes the state of the art on two well-known benchmarks, WikiQA and TREC-QA, achieving the impressive MAP scores of 92% and 94.3%, respectively, which largely outperform the the highest scores of 83.4% and 87.5% of previous work. We empirically show that TandA generates more stable and robust models reducing the effort required for selecting optimal hyper-parameters. Additionally, we show that the transfer step of TandA makes the adaptation step more robust to noise. This enables a more effective use of noisy datasets for fine-tuning. Finally, we also confirm the positive impact of TandA in an industrial setting, using domain specific datasets subject to different types of noise.

Question Answering in Restricted Domains: An Overview

2007 ◽  
Vol 33 (1) ◽  
pp. 41-61 ◽  
Author(s):  
Diego Mollá ◽  
José Luis Vicedo
Keyword(s):  
Research And Development ◽  
Historical Perspective ◽  
Question Answering ◽  
Knowledge Bases ◽  
Specific Information ◽  
Research Issues ◽  
Computing Power ◽  
Domain Specific ◽  
Text Collections ◽  
Restricted Domains

Automated question answering has been a topic of research and development since the earliest AI applications. Computing power has increased since the first such systems were developed, and the general methodology has changed from the use of hand-encoded knowledge bases about simple domains to the use of text collections as the main knowledge source over more complex domains. Still, many research issues remain. The focus of this article is on the use of restricted domains for automated question answering. The article contains a historical perspective on question answering over restricted domains and an overview of the current methods and applications used in restricted domains. A main characteristic of question answering in restricted domains is the integration of domain-specific information that is either developed for question answering or that has been developed for other purposes. We explore the main methods developed to leverage this domain-specific information.

scholarly journals Querying NoSQL with Deep Learning to Answer Natural Language Questions

2019 ◽  
Vol 33 ◽  
pp. 9416-9421
Author(s):  
Sebastian Blank ◽  
Florian Wilhelm ◽  
Hans-Peter Zorn ◽  
Achim Rettinger
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Natural Language ◽  
Question Answering ◽  
Query Languages ◽  
Domain Specific ◽  
Nosql Database ◽  
End To End ◽  
Database Operations ◽  
Almost All

Almost all of today’s knowledge is stored in databases and thus can only be accessed with the help of domain specific query languages, strongly limiting the number of people which can access the data. In this work, we demonstrate an end-to-end trainable question answering (QA) system that allows a user to query an external NoSQL database by using natural language. A major challenge of such a system is the non-differentiability of database operations which we overcome by applying policy-based reinforcement learning. We evaluate our approach on Facebook’s bAbI Movie Dialog dataset and achieve a competitive score of 84.2% compared to several benchmark models. We conclude that our approach excels with regard to real-world scenarios where knowledge resides in external databases and intermediate labels are too costly to gather for non-end-to-end trainable QA systems.

Sign in / Sign up

Export Citation Format

Share Document