Articles

In the past decade, several researchers have started reinvestigating the use of sub-phonetic models for lexical represen- tations within automatic speech recogni- tion systems. Lest history repeat itself, it may be instructive to mine the further past for models of lexical representations in the lexical access literature. In this work, …...

We present a novel almost-unsupervised approach to the task of Word Sense Dis- ambiguation (WSD). We build sense ex- amples automatically, using large quanti- ties of Chinese text, and English-Chinese and Chinese-English bilingual dictionar- ies, taking advantage of the observation that mappings between words and mean- ings are often different …...

While there have been many successful applica- tions of machine learning methods to tasks in NLP, learning algorithms are not typically designed to optimize NLP performance metrics. This paper evaluates an ensemble selection framework de- signed to optimize arbitrary metrics and automate the process of algorithm selection and parameter tuning. …...

MONA is an automata toolkit provid- ing a compiler for compiling formulae of monadic second order logic on strings or trees into string automata or tree automata. In this paper, we evaluate the option of using MONA as a treebank query tool. Unfortunately, we find that MONA is not an …...

German has a productive morphology and allows the creation of complex words which are often highly ambiguous. This paper reports on the development of a head-lexicalized PCFG for the disam- biguation of German morphological anal- yses. The grammar is trained on unla- beled data using the Inside-Outside algo- rithm. The …...

We have studied how context specific web corpus can be automatically created and mined for discovering semantic similarity relationships between terms (words or phrases) from a given collection of documents (target collection). These relationships between terms can be used to adjust the standard vectors space representation so as to improve …...

We describe a new method for the repre- sentation of NLP structures within rerank- ing approaches. We make use of a condi- tional log–linear model, with hidden vari- ables representing the assignment of lexi- cal items to word clusters or word senses. The model learns to automatically make these assignments …...

We formalize weighted dependency pars- ing as searching for maximum spanning trees (MSTs) in directed graphs. Using this representation, the parsing algorithm of Eisner (1996) is sufficient for search- ing over all projective trees in O(n3) time. More surprisingly, the representation is extended naturally to non-projective pars- ing using Chu-Liu-Edmonds …...

Traditional approaches to Information Ex- traction (IE) from speech input simply consist in applying text based methods to the output of an Automatic Speech Recog- nition (ASR) system. If it gives satis- faction with low Word Error Rate (WER) transcripts, we believe that a tighter inte- gration of the IE …...

Key phrases are usually among the most information-bearing linguistic structures. Translating them correctly will improve many natural language processing appli- cations. We propose a new framework to mine key phrase translations from web corpora. We submit a source phrase to a search engine as a query, then expand queries by …...

This paper presents a bidirectional in- ference algorithm for sequence label- ing problems such as part-of-speech tag- ging, named entity recognition and text chunking. The algorithm can enumerate all possible decomposition structures and find the highest probability sequence to- gether with the corresponding decomposi- tion structure in polynomial time. We …...

We present a part-of-speech tagger which introduces two new concepts: virtual evi- dence in the form of an “observed child” node, and negative training data to learn the conditional probabilities for the ob- served child. Associated with each word is a flexible feature-set which can in- clude binary flags, neighboring …...

Existing named entity (NE) transliteration approaches often exploit a general model to transliterate NEs, regardless of their origins. As a result, both a Chinese name and a French name (assuming it is already trans- lated into Chinese) will be translated into English using the same model, which often leads to …...

There has been little prior work on Named Entity Recognition for ”informal” docu- ments like email. We present two meth- ods for improving performance of per- son name recognizers for email: email- specific structural features and a recall- enhancing method which exploits name repetition across multiple documents. 1...

Many proper names are spelled inconsis- tently in speech recognizer output, posing a problem for applications where locating mentions of named entities is critical. We model the distortion in the spelling of a name due to the speech recognizer as the effect of a noisy channel. The models fol- low …...

This paper proposes a hybrid Chinese named entity recognition model based on multiple features. It differentiates from most of the previous approaches mainly as follows. Firstly, the proposed Hybrid Model integrates coarse particle feature (POS Model) with fine particle feature (Word Model), so that it can overcome the disadvantages of …...

Finite-state approaches have been highly successful at describ- ing the morphological processes of many languages. Such approaches have largely focused on modeling the phone- or character-level processes that generate candidate lexical types, rather than tokens in context. For the full analysis of words in context, disambiguation is also required (Hakkani-T¨ur …...

This paper introduces a graph-based algo- rithm for sequence data labeling, using ran- dom walks on graphs encoding label de- pendencies. The algorithm is illustrated and tested in the context of an unsuper- vised word sense disambiguation problem, and shown to significantly outperform the accuracy achieved through individual label assignment, …...

Recent studies into Web retrieval have shown that word sense disambiguation can increase retrieval effectiveness. How- ever, it remains unclear as to the mini- mum disambiguation accuracy required and the granularity with which one must define word sense in order to maximize these benefits. This study answers these questions using …...

“Bootstrapping” methods for learning require a small amount of supervision to seed the learning process. We show that it is sometimes possible to eliminate this last bit of supervision, by trying many candidate seeds and selecting the one with the most plausible outcome. We discuss such “strapping” methods in general, …...