Articles

Ordering information is a critical task for natural language generation applications. In this paper we propose an approach to information ordering that is particularly suited for text-to-text generation. We de- scribe a model that learns constraints on sentence order from a corpus of domain- specific texts and an algorithm that …...

This paper presents a dependency language model (DLM) that captures linguistic con- straints via a dependency structure, i.e., a set of probabilistic dependencies that express the relations between headwords of each phrase in a sentence by an acyclic, planar, undirected graph. Our contributions are three-fold. First, we incorporate the de- …...

Phrasal Verbs are an important feature of the English language. Properly identifying them provides the basis for an English parser to decode the related structures. Phrasal verbs have been a challenge to Natural Language Processing (NLP) because they sit at the borderline between lexicon and syntax. Traditional NLP frameworks that …...

Supertagging is the tagging process of assigning the correct elementary tree of LTAG, or the correct supertag, to each word of an input sentence1. In this pa- per we propose to use supertags to expose syntactic dependencies which are unavail- able with POS tags. We first propose a novel method …...

This paper proposes a hybrid of hand- crafted rules and a machine learning method for chunking Korean. In the par- tially free word-order languages such as Korean and Japanese, a small number of rules dominate the performance due to their well-developed postpositions and endings. Thus, the proposed method is primarily …...

We train a decision tree inducer (CART) and a memory-based classifier (MBL) on predicting prosodic pitch accents and breaks in Dutch text, on the basis of shal- low, easy-to-compute features. We train the algorithms on both tasks individu- ally and on the two tasks simultaneously. The parameters of both algorithms …...

This paper describes two methods for de- tecting word segments and their morpho- logical information in a Japanese sponta- neous speech corpus, and describes how to tag a large spontaneous speech corpus accurately by using the two methods. The first method is used to detect any type of word segments. …...

Noun extraction is very important for many NLP applications such as informa- tion retrieval, automatic text classification, and information extraction. Most of the previous Korean noun extraction systems use a morphological analyzer or a Part- of-Speech (POS) tagger. Therefore, they require much of the linguistic knowledge such as morpheme dictionaries …...

This paper describes a method for learn- ing the countability preferences of English nouns from raw text corpora. The method maps the corpus-attested lexico-syntactic properties of each noun onto a feature vector, and uses a suite of memory-based classifiers to predict membership in 4 countability classes. We were able to …...

When rules of transfer-based machine translation (MT) are automatically ac- quired from bilingual corpora, incor- rect/redundant rules are generated due to acquisition errors or translation variety in the corpora. As a new countermeasure to this problem, we propose a feedback cleaning method using automatic evalua- tion of MT quality, which …...

We present a novel approach for find- ing discontinuities that outperforms pre- viously published results on this task. Rather than using a deeper grammar for- malism, our system combines a simple un- lexicalized PCFG parser with a shallow pre-processor. This pre-processor, which we call a trace tagger, does surprisingly well …...

An investment of effort over the last two years has begun to produce a wealth of data concerning computational psycholin- guistic models of syntax acquisition. The data is generated by running simulations on a recently completed database of word order patterns from over 3,000 abstract languages. This article presents the …...

A central problem of word sense disam- biguation (WSD) is the lack of manually sense-tagged data required for supervised learning. In this paper, we evaluate an ap- proach to automatically acquire sense- tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 …...

We approximate Arabic’s rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to …...

This paper presents an unsupervised learn- ing approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. No parallel text is needed after the training phase. …...

We present a large-scale meta evaluation of eight evaluation measures for both single-document and multi-document summarizers. To this end we built a corpus consisting of (a) 100 Million auto- matic summaries using six summarizers and baselines at ten summary lengths in both English and Chinese, (b) more than 10,000 manual …...

Minimal Recursion Semantics (MRS) is the standard formalism used in large-scale HPSG grammars to model underspecified semantics. We present the first provably efficient algorithm to enumerate the read- ings of MRS structures, by translating them into normal dominance constraints. 1...

The model used by the CCG parser of Hockenmaier and Steedman (2002b) would fail to capture the correct bilexical dependencies in a language with freer word order, such as Dutch. This paper argues that probabilistic parsers should therefore model the dependencies in the predicate-argument structure, as in the model of …...

This paper is concerned with learning cat- egorial grammars in Gold’s model. contrast to k-valued classical categorialIn grammars, k-valued Lambek grammars are not learnable from strings. This re- sult was shown for several variants but the question was left open for the weak- est one, the non-associative variant NL. We …...

This paper presents a new bootstrapping approach to named entity (NE) classification. This approach only requires a few common noun/pronoun seeds that correspond to the concept for the target NE type, e.g. he/she/man/woman for PERSON NE. The entire bootstrapping procedure is implemented as training two successive learners: (i) a decision …...