TokenQuery is a query language over any labeled text (sequence of tokens); very similar to regular expressions but on top of tokens. TokenQuery can be viewed as an interface to query for specific patterns in a sequence of tokens using information provided by diverse NLP engines.

What is a Token?

In order to process text (natural language text), the common approach for natural language processing (NLP) is to break the text down into smaller processing units (tokens). Options include phonemes, morphemes, lexical information, phrases, sentences, or paragraphs. For example, this sentence :President Obama delivered his Farewell Address in Chicago on January 10, 2017. can be divided into tokens shown in blue highlights.

TokenQuery enables us to

• Combine labels from different NLP engines
• Query and reasoning over tokenized text
• Defining extentions for desired query functions

The inital idea came from Angel Chang and Christopher Manning presented in this paper. They have implemeneted it (TOKENSREGEX) in Java inside Stanford CoreNLP software package. Our version uses a different language for the query which is extensible, more structured, and supporting more features.

TokenQuery language

The language is defined as follow. Each query consists of a group of tokens shown each inside [ ]s. If you want to use ] inside your token matches you can simply use \ to skip.

[expr_for_token1][expr_for_token2][expr_for_token3]

which means we are searching for a sequence of three tokens that the first token satisfies the condition provided by expr_for_token1, the second token satisfies the condition provided by expr_for_token2 and so on.

Quantifiers

Likewise regular expressions, you can use quantifiers to have more compact queries. For example, the following query will match zero or more tokens satisfying the condition provided by expr_for_token1 followed by another token satisfies condition provided by expr_for_token2.

[expr_for_token1]*[expr_for_token2]

Capturing Groups

Like reguar expressions, you can define capturing groups by parentheses. for example ([expr_for_token1]+) [expr_for_token2] [expr_for_token3] returns a group containing sequence of tokens with satisfies the condition provided by expr_for_token1. Hence, ([expr_for_token1]+) [expr_for_token2] ([expr_for_token3]) returns two groups (chunk1 and chunk2) with a list of tokens matched inside each parentheses. You can also use named capturing by using (name <desired_pattern>). For example (name [expr_for_token1]) captures results under the name of name. If you don't provide any, it will capture all as a single group; in other words, [expr_for_token1]+ [expr_for_token2] [expr_for_token3] is equal to ([expr_for_token1]+ [expr_for_token2] [expr_for_token3]).

Token Expression

Expressions (like expr_for_token1 in the above examples) can be viewed as a list of acceptors for each token.

Basic expressions

[label:operation(operation_input)] is the base unit for defining a token expression, which means running operation on the value of label for this token returns if we should accept this token or not. operation is a function that accepts a token and optional extra setting string (operation_input) and returns True or False. For example, [pos:str_eq(VBZ)] matches any token that has a label pos and the string value for that is equal to VBZ. str_eq is an standard string operation check if the string is equal the extra setting string. or [pos:str_reg(V.*)] matches any token that has a pos label and the value for that label matches regex V.*. (i.e. any verbs) Note: If you want to check if a label exists or not and you don't care about the value of the label you can simply use this [pos:str_reg(.*)]. If no label provided the default will consider the text of the token. For example, [str_reg(.*)] will match any token or [str_reg('painter')] matches any token that has 'painter' as text.

core operations (acceptors)

Here is the list of predefined operations. You can extend this framework with your own defined operations.

String

This package provides string operations described below.

Shortened versions
For the convinence of use, exact string match is possible by having the text you want to match inside "s. For example ["painter"] will match any token that its text is painter but not Painter. If you want to find tokens that matches a regex you can have your regex inside /s . For example [/an?/] matches tokens having text a or an. [/Al.*/] matches any token starting with Al. [/km|kilometers?/] matches km, kilometer and kilometers

Int

This package provides operations that will cast the value of labels into an integer and apply arithmetic operations on that.

Web

This package provides operations for capturing meaningful web patterns.

Date

This package provides operations for working with date and time info in iso format.

Vector

compound expressions

For each token is possible to compound several basic expressions to support more complex patterns. Compounding is done using ! (not), & (and) and | (or) symbols. For example, [!pos:str_reg(V.*)] means any token that it is not a verb. [pos:str_reg(V.*)&!str_eq(is)] matches any verb except is. The ! has the highest proiority and the & and | has same priority and right associative. You can change the priority by using parentheses.

!X and Y        <=>   ( (!(X)) and Y )
!(X and Y)      <=>   ( !(X and Y) )
!(X and Y) or Z <=>   ( ( !(X and Y) ) or Z )
(X and Y) or Z  <=>   ( ( X and Y) or Z )
X and Y or Z    <=>   ( X and (Y or Z) )

How to install

pip install tokenquery

It has been test to work on python 2.7+

How to use

You can use your own tokenizer and create tokens or use our nltk wrapper to do the tokenization (see examples). We highly recommend to use a tokenizer that provides start and end of each token in the original text and the normalized value. This is surprizing helpful for visualization and debugging. For instance NLTK PTB tokenizer does not provide these info; so we wrote an script to estimate these from the output for our goal. Yes, this tool can be seen as an attempt to combine different types of information provided by NLP technologies considering using same tokenization. Currently we have integration with NLTK tokenizer and POS tagger and we are working to connect it to Spacy and google NLP API.

NLP Examples

We belive a big portion of NLP information can be expressed in terms of labels on top of tokens. Here is a list of the ones currently we use and how we represent it.

• Part Of Speech tags (e.g. [pos:/V.*/])

• Lemma (e.g. [lemma:'be'])

• Named-Entity tags (e.g. [ner:"PERSON"])

• Brown clusters

  label	We	need	a	lawyer	.
  POS	PRP	VBP	DT	NN	.
  bcluster				1000001101000

  And we can query members inside a cluster by tokenquery like this: [bcluster:/100000110[0-1]+/]) which will match all of these and more. (for more info see Miller et al., NAACL 2004)

  word	code
  lawyer	1000001101000
  newspaperman	100000110100100
  stewardess	100000110100101
  toxicologist	10000011010011
  slang	1000001101010
  babysitter	100000110101100
  conspirator	1000001101011010
  womanizer	1000001101011011
  mailman	10000011010111
  salesman	100000110110000
  bookkeeper	1000001101100010
  troubleshooter	10000011011000110
  bouncer	10000011011000111
  technician	1000001101100100
  janitor	1000001101100101
  saleswoman	1000001101100110

• Word embeddings For word embeddings you can use exact match. You can also define fancy metrics for comparision like cosine similarity as an operation. implemente more like . e.g. [w2v:cos_sim(A0F892<0.5)])

Chunks and Phrases

For chunks we recommend to use IOB formatting.

• Noun phrases We use label N-PH for noun phrase, B-NP as a value for starting a noun phrase and I-NP for Continue of a noun phrase. Or you can use directly B-NP as lable and keep the value for the id of that phrase in your knowledge base if any.

Examples

Detecting name of painters

from tokenquery.nlp.tokenizer import Tokenizer
from tokenquery.nlp.pos_tagger import POSTagger
from tokenquery.tokenquery import TokenQuery

# Penn Tree Bank Tokenizer
tokenizer = Tokenizer('PTBTokenizer')
# NLTK POS tagger
pos_tagger = POSTagger()

# Test sentence
input_text = 'David is a painter and I work as a writer.'
# Tokenizing the sentence
input_tokens = tokenizer.tokenize(input_text)
# adding pos tags
input_tokens = pos_tagger.tag(input_tokens)

# token regex to extract name of the painters
token_query_1 = TokenQuery('([pos:"NNP"]) [pos:"VBZ"] [/an?/] ["painter"]')
token_query_1.match_tokens(input_tokens)

# lets change the sentence
input_text = 'David is a famous painter and I work as a writer.'
input_tokens = tokenizer.tokenize(input_text)
input_tokens = pos_tagger.tag(input_tokens)

# because of `famous` now your token regex 1 isn't working anymore
token_query_1.match_tokens(input_tokens)

# Adding possible adjectives
token_query_2 = TokenQuery('([pos:"NNP"]) [pos:"VBZ"] [/an?/] [pos:"JJ"]* ["painter"]')
token_query_2.match_tokens(input_tokens)

# You can add labels directly
input_tokens[0].add_a_label('ner', 'PERSON')

# A mixture of labels will give you the same result
token_query_3 = TokenQuery('([ner:"PERSON"]) [pos:"VBZ"] [/an?/] [pos:"JJ"]* ["painter"]')
token_query_3.match_tokens(input_tokens)

# To cover names with more tokens
token_query_4 = TokenQuery('([ner:"PERSON"]+) [pos:"VBZ"] [/an?/] [pos:"JJ"]* ["painter"]')
token_query_4.match_tokens(input_tokens)