NLP - NER - Entity Recognition

I have worked on NLP, and custom NER examples. This streamlit demo covers NER in domain context and multiple entities link

• Name Entity Recognition - Extract Organizations, People, Locations, and many other entities from long, free-text financial documents.
• Extract Financial Relationships - Automatically identify relationships between companies, products, and people – even when they are mentioned using aliases.
• Classify Financial Text - Classify texts into 77 banking-related categories like credit reports, mortgages, money transfers and more.
• Financial Sentiment Analysis - Identify positive, negative or neutral sentiments in financial news.
• Financial De-identification - De-identify and mask sensitive personal information in documents and images.

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NLP, Recruitment, AI - Automated Matching :) Eightfold - Analysis

A ton of zero-shot / few shot / data labeling to customize and build models for

Some of the Key features / NLP overlaps

Custom Entity Recognition

• Education
• Place
• Company 
• Domain
• Skills Extraction

Custom Embedding for

• Similar Projects Search
• Project Summarization
• Creating custom embedding for each domain

NLP + Ranking

• Ranking and retrieving based on location/salary/education
• Skill Distribution / Contribution
• Domain Extraction from Company names

Vision

• OCR + Vision for Content Retrieval

Search / Retrieval

• Vector database for Finetuning/indexing vector databases to search and retrieve the closest matches

From JD

• Familiar with Language models, and transformers like BERT, GPT-3, T-5 etc.
• Prior experience building and deploying machine learning models in production at scale
• Familiarity with MLOps tools and pipelines (MLflow, Metaflow).
• Integration with Workday, SuccessFactors, Taleo, PeopleSoft, iCIMS, SmartRecruiters,
• REST APIs, microservices, data ingestion and processing systems, and distributed systems.

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Map NLP Possibilities to Applied use cases

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Construction of English Resume Corpus and Test with Pre-trained Language Models

A Few-shot Approach to Resume Information Extraction via Prompts

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Conditional Random Fields - NER Notes

• NER - A named entity is a “real-world object” that’s assigned a name – for example, a person, a country, a product, or a book title. 
• CRF can take context into account.
• Each prediction is dependent only on its immediate neighbors. 
• CRF model to predict the conditional probability of Y by training the model parameters
• CRF builds transition probability that accounts for the likelihood of observing each transition between labels in the sequence
• CRF is a discriminative approach, It builds both likely transition and unlikely transitions
• A Discriminative model ‌models the decision boundary between the classes

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Feature Functions - Notes

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NER Approaches

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NLP - NER - Papers

Paper #1 - Recent Trends in Named Entity Recognition (NER)

Key Notes

• ‘Named Entity Recognition’ refers to identifying person, organization, location
• NER belongs to a general class of problems in NLP called sequence tagging 
• Prominent supervised learning methods - Hidden Markov Models (HMM), Decision Trees, Maximum Entropy Models (ME)

• Unsupervised clustering method using lexical resources eg. Wordnet

Paper #2 - A Survey on Deep Learning for Named Entity Recognition

Key Notes

• Rule-based approaches, which do not need annotated data as they rely on hand-crafted rules
• Unsupervised learning approaches, which rely on unsupervised algorithms Feature-based supervised learning approaches, which rely on supervised learning algorithms

• 71% of search queries contain at least one named entity

• word-level representation  - continuous bagof-words (CBOW) and continuous skip-gram models
• Commonly used word embeddings include Google Word2Vec, Stanford
• GloVe, Facebook fastText and SENNA.
• CharNER considers a sentence as a sequence of characters and utilizes LSTMs to extract characterlevel representations.
• Besides word-level and character-level representations, some studies also incorporate additional information (e.g., gazetteers [18], [108], lexical similarity [109], linguistic dependency [110] and visual features [111]) into the final representations of words

Paper #3 - Document Ranking for Curated Document Databases using BERT and Knowledge Graph Embeddings: Introducing GRAB-Rank

• Key Notes
• Curated Document Databases (CDD) play an important role in helping researchers find relevant articles in scientific literature
• Document ranking has been extensively used in the context of document retrieval
• Recent work on Learning to Rank (LETOR) has used word embeddings of various kind as the input
• Word embeddings can be learnt from scratch or a pre-trained embedding model can be adopted
• A popular algorithm for generating vector representations of words is GloVE (Global Vectors for Word Representation), an unsupervised learning algorithm that operates by aggregating global word-word co-occurrence statistics
• Semantic document ranking models take into account the context of terms in relation to their neighbouring terms
• Context of the word “bank”, either as: (i) an organisation for investing and borrowing money, (ii) the side of a river or lake, (iii) a long heap of some substance
• A popular choice of pre-trained contextual model is the Bidirectional Encoder Representations from Transformer (BERT)
• An alternative contextual model that can be used is the embeddings from Language Model ELMo
• A knowledge graph is a collection of vertices and edges where the vertices represent entities or concepts, and the edges represent a relationship between entities and/or concepts. 

• OIE4KGC (Open Information Extraction for Knowledge Graph Construction)

More Reads

• Ad-hoc retrieval with BERT
• Multi-Stage Document Ranking with BERT
• Pretrained Transformers for Text Ranking: BERT and Beyond
• Composite Re-Ranking for Efficient Document Search with BERT
• CEDR: Contextualized Embeddings for Document Ranking

Code Experiments

Compare documents similarity using Python | NLP

Measuring the Document Similarity in Python

How to compute the similarity between two text documents?

How to do semantic document similarity using BERT

How to cluster text documents using BERT

Question answering using transformers and BERT

Calculating Document Similarities using BERT, word2vec, and other models

Pykg2vec representation of entities and relations in Knowledge Graphs

Pykg2vec: Python Library for KGE Methods

Python NLP Tutorial: Building A Knowledge Graph using Python and SpaCy

NER with spacy

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