assocentity

package module
Version: v10.0.0-...-f9fec74 Latest Latest
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Published: Nov 5, 2022 License: MIT Imports: 5 Imported by: 0

README

assocentity

Package assocentity returns the average distance from tokens to given entities. Important: If you use the provided NLP tokenizer, you can't use special characters in entities due its nature of tokenization.

Features

  • Tokenization customization
  • Entity aliases
  • Default NLP tokenizer (by Google)
  • Multi-OS CLI version

Installation

$ go get github.com/ndabAP/assocentity/v10

Prerequisites

Sign-up for a Cloud Natural Language API service account key and download the generated JSON file. This equals the credentialsFile at the example below. You should never commit that file.

Usage

import (
	"context"
	"fmt"

	"github.com/ndabAP/assocentity/v10"
	"github.com/ndabAP/assocentity/v10/nlp"
	"github.com/ndabAP/assocentity/v10/tokenize"
)

const credentialsFile = "google_nlp_service_account.json"

func main() {
	text := "Punchinello wanted Payne? He'd see the pain."
	entities := []string{"Punchinello", "Payne"}

	// Create a Google NLP instance
	nlpTok := nlp.NewNLPTokenizer(credentialsFile, nlp.AutoLang)

	// Allow any part of speech
	posDeterm := nlp.NewNLPPoSDetermer(tokenize.ANY)

	// Do calculates the average distances
	ctx := context.Background()
	assocEntities, err := assocentity.Do(ctx, nlpTok, posDeterm, text, entities)
	if err != nil {
		panic(err)
	}

	fmt.Println(assocEntities)
	// map[tokenize.Token]float64{
	// 	{
	// 		PoS:  tokenize.VERB,
	// 		Text: "wanted",
	// 	}: 1,
	// 	{
	// 		PoS:  tokenize.PUNCT,
	// 		Text: "?",
	// 	}: 2,
	// 	{
	// 		PoS:  tokenize.PRON,
	// 		Text: "He",
	// 	}: 3,
	// 	{
	// 		PoS:  tokenize.VERB,
	// 		Text: "'d",
	// 	}: 4,
	// 	{
	// 		PoS:  tokenize.VERB,
	// 		Text: "see",
	// 	}: 5,
	// 	{
	// 		PoS:  tokenize.DET,
	// 		Text: "the",
	// 	}: 6,
	// 	{
	// 		PoS:  tokenize.NOUN,
	// 		Text: "pain",
	// 	}: 7,
	// 	{
	// 		PoS:  tokenize.PUNCT,
	// 		Text: ".",
	// 	}: 8,
	// }
}

In-depth

Section "General workflow" explains the process from a non-technical perspective while section API is dedicated to developers.

General workflow

The process is split into three parts. Two of them belong to tokenization and one calculates the average distance between words and entities.

  1. Tokenization. Splits the tokens and assigns part of speech
  2. Part of speech determination. Keeps only the wanted part of speeches
  3. Calculating the average. Main function that does the actual work
Tokenization

Googles Cloud Natural Language API is the default tokenizer and will split the tokens, and after that assigns the part of speech to the tokens. No additional checking should be done here. Note: For this step, it's nessecary to sign-up for a service account key.

A simpler, offline solution would be using Gos native strings.Fields method as tokenizer.

Part of speech determination

It's possible to only allow certain part of speeches, e. g. only nouns and verbs. Also the entities must stay included. Therefore, this step is separated so it could be more optimized.

Calculating the average

Finally, the average distances get calculated with the given predecessors.

API

There are two possibilities to interfere into the tokenization process. You just need to implement the interfaces. Do takes the interfaces and calls their methods. For a non-technical explanation, read the procedure section.

Tokenization

Interface to implement:

type Tokenizer interface {
	Tokenize(ctx context.Context, text string) ([]Token, error)
}

While Token is of type:

type Token struct {
	PoS  PoS    // Part of speech
	Text string // Text
}

So, for example given this text:

text := "Punchinello was burning to get me"

The result from Tokenize would be:

[]Token{
	{
		Text: "Punchinello",
		PoS:  tokenize.NOUN,
	},
	{
		Text: "was",
		PoS:  tokenize.VERB,
	},
	{
		Text: "burning",
		PoS:  tokenize.VERB,
	},
	{
		Text: "to",
		PoS:  tokenize.PRT,
	},
	{
		Text: "get",
		PoS:  tokenize.VERB,
	},
	{
		Text: "me",
		PoS:  tokenize.PRON,
	},
}
Part of speech determination

Interface to implement:

type PoSDetermer interface {
	DetermPoS(textTokens []Token, entitiesTokens [][]Token) []Token
}

We want to preserve the part of speech information. Therefore, we return Token here instead of string. This makes it possible to keep the real distances between tokens, e. g.

Calculating the average

This step can't be changed. It takes a Tokenizer, PoSDetermer, text as string and entities in a form of [][]string. The method will call all the interface methods and returns a map with the tokens and distances.

CLI

There is also a terminal version available for either Windows, Mac (Darwin) or Linux (only with 64-bit support) if you don't have Go available. The application expects the text as stdin and accepts the following flags:

Flag Description Type Default
gog-svc-loc Google Clouds NLP JSON service account file, example: -gog-svc-loc="~/gog-svc-loc.json" string
pos Defines part of speeches to keep, example: -pos=noun,verb,pron string any
entities Define entities to be searched within input, example: -entities="Max Payne,Payne" string

Example:

echo "Relax, Max. You're a nice guy." | ./bin/assocentity_linux_amd64_v10.0.1-7-gdfeb0f1-dirty -gog-svc-loc=/home/max/.config/assocentity/google-service.json -entities="Max Payne,Payne,Max"

The application writes the result as CSV formatted string to stdout.

Projects using assocentity

  • entityscrape - Distance between word types (default: adjectives) in news articles and persons

Author

Julian Claus and contributors.

License

MIT

Documentation

Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func Do

func Do(
	ctx context.Context,
	tokenizer tokenize.Tokenizer,
	psd tokenize.PoSDetermer,
	text string,
	entities []string,
) (map[tokenize.Token]float64, error)

Do returns the average distance from entities to a text consisting of token

Types

This section is empty.

Source Files

Directories

Path Synopsis
internal

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