1 / 14

Enhancing Morphological Processing: FSAs, FSTs, and Stemming in Natural Language Processing

This article explores the application of Finite State Automata (FSAs) and Finite State Transducers (FSTs) in morphological processing and stemming within Natural Language Processing (NLP). It discusses the efficiency of FSAs for validating input strings, particularly in complex languages like Hungarian and Finnish. The article also examines the benefits and drawbacks of stemming in information retrieval, presenting metrics such as precision and recall to evaluate performance. Overall, it highlights the importance of morphological processing for effective NLP applications.

mika
Télécharger la présentation

Enhancing Morphological Processing: FSAs, FSTs, and Stemming in Natural Language Processing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Morphological Processing & Stemming Using FSAs/FSTs

  2. FSAs and Morphology • Can be used to validate/recognize input string • For example, consider the Spanish conjugation for amar in J&M p. 64 • What would a FSA look like the would recognize the input? a 3 5 s am e 1 2 … m 4 6 …

  3. FSTs and Morphology • An FST could output information about the input, such as a translation or grammatical info: a e a:ε 3 ε:impf am:love o:ε 1 2 … 7

  4. FSAs and NLP • Why even use FSAs in NLP? • Memory and storage are cheap • Build one large lexicon • List all entries and req’d output amo: amas: ames love love love pres ind pres impf pres subj • Some NLP apps do this (e.g., AZ Noun Phraser (Tolle 2001)) [ ] [ ] [ ]

  5. FSAs and NLP • For more morphologically complex languages, one big lexicon not feasible • Consider Hungarian and Finnish • One verbal form • Hundreds of possible inflections • Millions of resulting forms • A complete “word” lexicon not feasible • Morphological processing essential

  6. Hungarian • Consider one concept/’word’ in Hungarian: haz house hazat house (object) haznak of the house hazzal with the house hazza into a house hazba into the house hazra to the house …

  7. Hungarian • Now consider plural inflections: hazak houses hazakat houses (object) hazaknak of the houses hazakzal with the houses hazakza into a houses hazakba into the houses hazakra to the houses …

  8. Hungarian • And possessives: hazaim my houses hazaimat my houses (object) hazaimnak of the houses hazaimzal with the houses hazaimza into a houses hazaimba into the houses hazaimra to the houses …

  9. Stop

  10. Stemming • Used in many IR applications • For building equivalence classes Connect Connected Connecting Connection Connections Porter Stemmer, simple and efficient Website: http://www.tartarus.org/~martin/PorterStemmer Same class; suffixes irrelevant

  11. Stop

  12. Stemming and Performance • Does stemming help IR performance? • Harman 91 indicated that it hurt as much as it helped • Krovetz 93 shows that stemming does help • Porter-like algorithms work well with smaller documents • Krovetz proposes that stemming loses information • Derivational morphemes tell us something that helps identify word senses (and helps in IR) • Stemming them = information loss

  13. Evaluating Performance • Measures of Stemming Performance rely on similar metrics used in IR: • Precision: measure of the proportion of selected items the system got right • precision = tp / (tp + fp) • Recall: measure of the proportion of the target items the system selected • recall = tp / (tp + fn) • Rule of thumb: as precision increases, recall drops, and vice versa • Metrics widely adopted in Stat NLP

  14. Precision and Recall • Take a given stemming task • Suppose there are 100 words that could be stemmed • A stemmer gets 52 of these right (tp) • But it inadvertently stems 10 others (fp) Precision = 52 / (52 + 10) = .84 Recall = 52 / (52 + 48) = .52

More Related