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NLP Terms

POS = part of speech  
NP-chunking = noun phrase chunking  

DT = determiner
JJ = adjectives
NN = noun
VBD = verb

BLEU (bilingual evaluation understudy)

is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU.[1][2] BLEU was one of the first metrics to achieve a high correlation with human judgements of quality,[3][4] and remains one of the most popular automated and inexpensive metrics.

Word Embedding

Word Embedding turns text into numbers.

Types of Word Embedding

  1. Bag of Words - each word is represented in the matrix, results in sparse matrices
  2. GloVe - counts of co-occurrences
  3. Word2Vec
  • These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.

IE (Information Extraction)

IE turns the unstructured information embedded in texts into structured data.

IOB (Inside, Outside, Beginning)

The most widespread file representation uses IOB tags:
IOB = Inside-Outside-Begininning
B = beginnning (marks beginning of chunk)
I = inside (all subsequent parts of chunk)
O = outside

Named Entity

anything that can be referred to with a proper name

NER (Named Entity Recognition)

task of detecting and classifying all the proper names mentioned in a text

  • Generic NER: finds names of people, places and organizations that are mentioned in ordinary news texts
  • practical applications: built to detect everything from names of genes and proteins, to names of college courses

Reference Resolution (Coreference)

occurs when two or more expressions in a text refer to the same person or thing; they have the same referent, e.g. Bill said he would come; the proper noun Bill and the pronoun he refer to the same person, namely to Bill

Relation Detection and Classification

find and classify semantic relations among the entities discovered in a given text

Event Detection and Classification

find and classify the events in which the entities are participating

GloVe

GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.

Temporal Expression Detection

  • tells us that our sample text contains the temporal expressions Friday and Thursday
  • includes date expressions such as days of the week, months, holidays, as well as relative expressions including phrases like two days from now or next year.
  • includes time: noon, 3pm, etc.

Temporal Analysis

over problem is to map temporal expressions onto specific calendar dates or times of day and then to use those times to situate events in time.