Memory-Based Shallow Parsing

We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as memory-based modules. The experiments reported in this paper show competitive results, the F-value for the Wall Street Journal (WSJ) treebank is: 93.8% for NP chunking, 94.7% for VP chunking, 77.1% for subject detection and 79.0% for object detection.Comment: 8 pages, to appear in: Proceedings of the EACL'99 workshop on Computational Natural Language Learning (CoNLL-99), Bergen, Norway, June 199

A Spoken Dialogue System for Enabling Comfortable Information Acquisition and Consumption

早大学位記番号:新8137早稲田大

Larger-first partial parsing

Larger-first partial parsing is a primarily top-down approach to partial parsing that is opposite to current easy-first, or primarily bottom-up, strategies. A rich partial tree structure is captured by an algorithm that assigns a hierarchy of structural tags to each of the input tokens in a sentence. Part-of-speech tags are first assigned to the words in a sentence by a part-of-speech tagger. A cascade of Deterministic Finite State Automata then uses this part-of-speech information to identify syntactic relations primarily in a descending order of their size. The cascade is divided into four specialized sections: (1) a Comma Network, which identifies syntactic relations associated with commas; (2) a Conjunction Network, which partially disambiguates phrasal conjunctions and llly disambiguates clausal conjunctions; (3) a Clause Network, which identifies non-comma-delimited clauses; and (4) a Phrase Network, which identifies the remaining base phrases in the sentence. Each automaton is capable of adding one or more levels of structural tags to the tokens in a sentence. The larger-first approach is compared against a well-known easy-first approach. The results indicate that this larger-first approach is capable of (1) producing a more detailed partial parse than an easy first approach; (2) providing better containment of attachment ambiguity; (3) handling overlapping syntactic relations; and (4) achieving a higher accuracy than the easy-first approach. The automata of each network were developed by an empirical analysis of several sources and are presented here in detail

Μηχανική Μάθηση στην Επεξεργασία Φυσικής Γλώσσας

Η διατριβή εξετάζει την χρήση τεχνικών μηχανικής μάθησης σε διάφορα στάδια της επεξεργασίας φυσικής γλώσσας, κυρίως για σκοπούς εξαγωγής πληροφορίας από κείμενα. Στόχος είναι τόσο η βελτίωση της προσαρμοστικότητας των συστημάτων εξαγωγής πληροφορίας σε νέες θεματικές περιοχές (ή ακόμα και γλώσσες), όσο και η επίτευξη καλύτερης απόδοσης χρησιμοποιώντας όσο το δυνατό λιγότερους πόρους (τόσο γλωσσικούς όσο και ανθρώπινους). Η διατριβή κινείται σε δύο κύριους άξονες: α) την έρευνα και αποτίμηση υπαρχόντων αλγορίθμων μηχανικής μάθησης κυρίως στα στάδια της προ-επεξεργασίας (όπως η αναγνώριση μερών του λόγου) και της αναγνώρισης ονομάτων οντοτήτων, και β) τη δημιουργία ενός νέου αλγορίθμου μηχανικής μάθησης και αποτίμησής του, τόσο σε συνθετικά δεδομένα, όσο και σε πραγματικά δεδομένα από το στάδιο της εξαγωγής σχέσεων μεταξύ ονομάτων οντοτήτων. Ο νέος αλγόριθμος μηχανικής μάθησης ανήκει στην κατηγορία της επαγωγικής εξαγωγής γραμματικών, και εξάγει γραμματικές ανεξάρτητες από τα συμφραζόμενα χρησιμοποιώντας μόνο θετικά παραδείγματα.This thesis examines the use of machine learning techniques in various tasks of natural language processing, mainly for the task of information extraction from texts. The objectives are the improvement of adaptability of information extraction systems to new thematic domains (or even languages), and the improvement of their performance using as fewer resources (either linguistic or human) as possible. This thesis has examined two main axes: a) the research and assessment of existing algorithms of machine learning mainly in the stages of linguistic pre-processing (such as part of speech tagging) and named-entity recognition, and b) the creation of a new machine learning algorithm and its assessment on synthetic data, as well as in real world data from the task of relation extraction between named entities. This new algorithm belongs to the category of inductive grammar learning, and can infer context free grammars from positive examples only