Sentence Level Text Analysis

Simon speaks about sex with Britney Spears

Example

Sentence level analysis

Natural language syntax

• describes relationships among words

Automatic syntactic analysis

• revealing inter-word relationships on various levels
• detection of noun (prepositional, verb, ...) phrases, clauses

• | Simon | spoke | about sex | with Britney Spears |
• | Simon | spoke | about sex with Britney Spears |

Syntactic trees

Why are we doing this?

Syntactic units are carriers of meaning

• “in the city”
• meaning of “in”, “the” is unclear, complicated
• meaning of “in the city” is simply where

Words are not enough

• red brick house vs. brick house red vs. red house brick
• Honey, give me love vs. Love, give me honey

Starting point for intelligent natural language applications

• extraction of facts & question answering
• logical analysis
• punctuation detection & grammar checking
• natural text generation
• authorship detection
• machine translation

Example: Extraction of facts

Example: Logical analysis

Example: Grammar checking

• Let’s eat grandma'''
  • syntactic analysis
  • detection of non-probable constructions
  • -> grandma is not a usual object of eating
  • -> correction suggestion

• Let’s eat, grandma'''
  • life saved :)

Similarly with other grammar phenomena

• “This is worth try”

->

• “This is worth trying”

How to analyse natural language syntax?

Prerequisites

• word level analysis (part of speech, gender, number)
• named entity recognition
• common sense information (e.g. “pregnant” goes with women only)

Named entity recognition

• determine that e.g. “prof. Václav Šplíchal” is a person
• can be viewed as a sub-task of syntactic analysis

Statistical methods

• people annotate corpus
• statistic methods learn rules from the corpus
• universal across languages (to some extent)
• annotation is expensive
• hard to customize for different applications
• data are usually not big enough

Rule-based methods

• specialists develop a set of rules (“grammar”)
• not universal, depends on specialists
• grammar can become uneasy to maintain
• easy to customize for different applications

Hybrids

Syntactic analysers in the NLP Centre

Synt

• C++, fast (0.07 s/sentence)
• based on an expressive meta-grammar

SET

• Python, slower but easily adaptable
• based on a set of phrase patterns

Synt+SET

• rule-based backbone with statistical extensions
• grammars for Czech, English and Slovak
• accuracy 85–90 % on newspaper texts

Word Sketches

• very fast shallow syntax for large corpora
• 31 languages

Conclusions

Sentence level analysis

• detection of phrases and inter-word relationships
• their further processing

Applications

• grammar checking
• information analysis of text
• text generation