Category: NLP

Articles About Natural Language Processing

NLP

Deep dive into multi-label classification..! (With detailed Case Study)

We first convert the comments to lower-case and then use custom made functions to remove html-tags, punctuation and non-alphabetic characters from the comments. import nltkfrom nltk.corpus import stopwordsfrom nltk.stem.snowball import SnowballStemmerimport reimport sysimport warningsdata = data_rawif not sys.warnoptions:warnings.simplefilter(“ignore”)def cleanHtml(sentence):cleanr = re.compile(”)cleantext = re.sub(cleanr, ‘ ‘, str(sentence))return cleantextdef cleanPunc(sentence): #function to clean the word of any punctuation or special characterscleaned Visit source site to finish reading.

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NLP

Building a Simple Chatbot from Scratch in Python (using NLTK)

A chatbot is an artificial intelligence-powered piece of software in a device (Siri, Alexa, Google Assistant etc), application, website or other networks that try to gauge consumer’s needs and then assist them to perform a particular task like a commercial transaction, hotel booking, form submission etc . Today almost every company has a chatbot deployed to engage with the users. Some of the ways in which companies are using chatbots are: To deliver flight information to connect customers and their […]

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NLP, The Neural MT Weekly

Issue #100 – Celebrating a century of Neural MT Weekly posts

24 Sep20 Issue #100 – Celebrating a century of Neural MT Weekly posts Author: Dr. Carla Parra Escartín, Global Program Manager @ Iconic Hello, again! When we started The Neural MT Weekly back in July 2018 it was originally intended to be an 8-part series…and here we are, 100 issues later! It’s a great milestone to achieve and it’s a testament to the authors, the topics they choose, the quality and accessibility of their writing. Across the 100 issues, we’ve […]

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NLP, Semantic Similarity

Issue #99 – Training Neural Machine Translation with Semantic Similarity

17 Sep20 Issue #99 – Training Neural Machine Translation with Semantic Similarity Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction The standard way of training Neural Machine Translation (NMT) systems is by Maximum Likelihood Estimation (MLE), and although there have been experiments in the past to optimize systems directly in order to improve particular evaluation metrics, these were of limited success. Of course, using BLEU is not ideal due to the fact that it fails to account for […]

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Context-aware Neural MT, NLP

Issue #98 – Unified and Multi-encoders for Context-aware Neural MT

10 Sep20 Issue #98 – Unified and Multi-encoders for Context-aware Neural MT Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction Context-aware Neural MT uses context information to perform document-level translation or domain adaptation. The context of surrounding sentences allows the model to capture discourse phenomena. The context of similar sentences can also be useful to dynamically adapt the translation to a domain. In this post, we take a look at two papers which compare uni-encoder and multi-encoder […]

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NLP, Target Conditioned Sampling

Issue #97 – Target Conditioned Sampling: Optimising Data Selection for Multilingual Neural Machine Translation

03 Sep20 Issue #97 – Target Conditioned Sampling: Optimising Data Selection for Multilingual Neural Machine Translation Author: Dr. Chao-Hong Liu, Machine Translation Scientist @ Iconic Introduction It is known that neural machine translation (NMT) is particularly tricky in the case of low-resource languages. Thus, it is not surprising that researchers are actively investigating how to improve the performance on NMT systems for low-resource languages and many approaches are currently being explored. In issue #88 of our blog we reviewed a […]

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annotations, NLP

Issue #96 – Using Annotations for Machine Translating Named Entities

27 Aug20 Issue #96 – Using Annotations for Machine Translating Named Entities Author: Dr. Carla Parra Escartín, Global Program Manager @ Iconic Introduction Getting the translation of named entities right is not a trivial task and Machine Translation (MT) has traditionally struggled with it. If a named entity is wrongly translated, the human eye will quickly spot it, and more often than not, those mistranslations will make people burst into laughter as machines can, seemingly, be very creative. To a […]

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constrained parameter initialisation, NLP

Issue #95 – Constrained Parameter Initialisation for Deep Transformers in Neural MT

20 Aug20 Issue #95 – Constrained Parameter Initialisation for Deep Transformers in Neural MT Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction As the Transformer model is the state of the art in Neural MT, researchers have tried to build wider (with higher dimension vectors) and deeper (with more layers) Transformer networks. Wider networks are more costly in terms of training and generation time, thus they are not the best option in production environments. However, adding encoder […]

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bilingual word embeddings, NLP

Issue #94 – Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation

13 Aug20 Issue #94 – Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation Author: Dr. Chao-Hong Liu, Machine Translation Scientist @ Iconic Introduction Curating corpora of quality sentence pairs is a fundamental task to building Machine Translation (MT) systems. This resource can be availed from Translation Memory (TM) systems where the human translations are recorded. However, in most cases we don’t have TM databases but comparable corpora, e.g. news articles of the same story in different languages. […]

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AMR, NLP

Issue #93 – Semantic Neural Machine Translation using AMR

06 Aug20 Issue #93 – Semantic Neural Machine Translation using AMR Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction Semantic representations were part of the very early Machine Translation (MT) systems, yet have had little role in recent Neural MT (NMT) systems. Given that a good translation should reflect the meaning of the source text, this seems an important area to focus on, particularly since the abstraction could potentially help handle data sparsity. In today’s blog post, we […]

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