Abstract:
We address the problem of automatically constructing a thesaurus (hierarchically clustering words) based on corpus data. We view the problem of clustering words as that of estimating a joint distribution over the Cartesian product of a partition of a set of nouns and a partition of a set of verbs, and propose an estimation algorithm using simulated annealing with an energy function based on the Minimum Description Length (MDL) Principle. We empirically compared the performance of our method based on the MDL Principle against that of one based on the Maximum Likelihood Estimator, and found that the former outperforms the latter. We also evaluated the method by conducting pp-attachment disambiguation experiments using an automatically constructed thesaurus. Our experimental results indicate that we can improve accuracy in disambiguation by using such a thesaurus.

Abstract:
We address the problem of automatically acquiring case frame patterns (selectional patterns) from large corpus data. In particular, we propose a method of learning dependencies between case frame slots. We view the problem of learning case frame patterns as that of learning multi-dimensional discrete joint distributions, where random variables represent case slots. We then formalize the dependencies between case slots as the probabilistic dependencies between these random variables. Since the number of parameters in a multi-dimensional joint distribution is exponential, it is infeasible to accurately estimate them in practice. To overcome this difficulty, we settle with approximating the target joint distribution by the product of low order component distributions, based on corpus data. In particular we propose to employ an efficient learning algorithm based on the MDL principle to realize this task. Our experimental results indicate that for certain classes of verbs, the accuracy achieved in a disambiguation experiment is improved by using the acquired knowledge of dependencies.

Abstract:
We consider the problem of learning co-occurrence information between two word categories, or more in general between two discrete random variables taking values in a hierarchically classified domain. In particular, we consider the problem of learning the `association norm' defined by A(x,y)=p(x, y)/(p(x)*p(y)), where p(x, y) is the joint distribution for x and y and p(x) and p(y) are marginal distributions induced by p(x, y). We formulate this problem as a sub-task of learning the conditional distribution p(x|y), by exploiting the identity p(x|y) = A(x,y)*p(x). We propose a two-step estimation method based on the MDL principle, which works as follows: It first estimates p(x) as p1 using MDL, and then estimates p(x|y) for a fixed y by applying MDL on the hypothesis class of {A * p1 | A \in B} for some given class B of representations for association norm. The estimation of A is therefore obtained as a side-effect of a near optimal estimation of p(x|y). We then apply this general framework to the problem of acquiring case-frame patterns. We assume that both p(x) and A(x, y) for given y are representable by a model based on a classification that exists within an existing thesaurus tree as a `cut,' and hence p(x|y) is represented as the product of a pair of `tree cut models.' We then devise an efficient algorithm that implements our general strategy. We tested our method by using it to actually acquire case-frame patterns and conducted disambiguation experiments using the acquired knowledge. The experimental results show that our method improves upon existing methods.

Abstract:
We address the problem of clustering words (or constructing a thesaurus) based on co-occurrence data, and using the acquired word classes to improve the accuracy of syntactic disambiguation. We view this problem as that of estimating a joint probability distribution specifying the joint probabilities of word pairs, such as noun verb pairs. We propose an efficient algorithm based on the Minimum Description Length (MDL) principle for estimating such a probability distribution. Our method is a natural extension of those proposed in (Brown et al 92) and (Li & Abe 96), and overcomes their drawbacks while retaining their advantages. We then combined this clustering method with the disambiguation method of (Li & Abe 95) to derive a disambiguation method that makes use of both automatically constructed thesauruses and a hand-made thesaurus. The overall disambiguation accuracy achieved by our method is 85.2%, which compares favorably against the accuracy (82.4%) obtained by the state-of-the-art disambiguation method of (Brill & Resnik 94).

Abstract:
We address the problem of automatically acquiring case-frame patterns from large corpus data. In particular, we view this problem as the problem of estimating a (conditional) distribution over a partition of words, and propose a new generalization method based on the MDL (Minimum Description Length) principle. In order to assist with the efficiency, our method makes use of an existing thesaurus and restricts its attention on those partitions that are present as `cuts' in the thesaurus tree, thus reducing the generalization problem to that of estimating the `tree cut models' of the thesaurus. We then give an efficient algorithm which provably obtains the optimal tree cut model for the given frequency data, in the sense of MDL. We have used the case-frame patterns obtained using our method to resolve pp-attachment ambiguity.Our experimental results indicate that our method improves upon or is at least as effective as existing methods.

In wheat plants at the vegetative growth stage, the shoot apical meristem (SAM) produces leaf primordia. When reproductive growth is initiated, the SAM forms an inflorescence meristem (IM) that differentiates a series of spikelet meristem (SM) as the branch. The SM then produces a series of floret meristem (FM) as the branch. To identify the mechanisms that regulate formation of the reproductive meristems in wheat, we have investigated a leaf initiation mutant, fushi-darake(fdk) which was developed by ion beam mutagenesis. The morphological traits were compared in wild type (WT) and fdk mutant plants grown in the experimental field. WT plants initiated leaves from SAM at regular intervals in spiral phyllotaxy, while fdk plants had 1/2 alternate phyllotaxy with rapid leaf emergence. The fdk plants have increased numbers of nodes and leaves compared with WT plants. The time interval between successive leaf initiation events (plastochron) was measured in plants grown in a growth chamber. The fdk plants clearly show the rapid leaf emergence, indicating a shortened plastochron. Each tiller in fdk plants branches at the upper part of the culm. The fine structure of organ formation in meristems of fdk plants was examined by scanning electron microscopy (SEM). The SEM analysis indicated that fdk plants show transformation of spikelet meristems into vegetative shoot meristems. In conclusion, the fdk mutant has a heterochronic nature, i.e., both

Abstract:
We consider the problem of learning a certain type of lexical semantic knowledge that can be expressed as a binary relation between words, such as the so-called sub-categorization of verbs (a verb-noun relation) and the compound noun phrase relation (a noun-noun relation). Specifically, we view this problem as an on-line learning problem in the sense of Littlestone's learning model in which the learner's goal is to minimize the total number of prediction mistakes. In the computational learning theory literature, Goldman, Rivest and Schapire and subsequently Goldman and Warmuth have considered the on-line learning problem for binary relations R : X * Y -> {0, 1} in which one of the domain sets X can be partitioned into a relatively small number of types, namely clusters consisting of behaviorally indistinguishable members of X. In this paper, we extend this model and suppose that both of the sets X, Y can be partitioned into a small number of types, and propose a host of prediction algorithms which are two-dimensional extensions of Goldman and Warmuth's weighted majority type algorithm proposed for the original model. We apply these algorithms to the learning problem for the `compound noun phrase' relation, in which a noun is related to another just in case they can form a noun phrase together. Our experimental results show that all of our algorithms out-perform Goldman and Warmuth's algorithm. We also theoretically analyze the performance of one of our algorithms, in the form of an upper bound on the worst case number of prediction mistakes it makes.

Abstract:
Dominance hierarchy among animals is widespread in various species and believed to serve to regulate resource allocation within an animal group. Unlike small groups, however, detection and quantification of linear hierarchy in large groups of animals are a difficult task. Here, we analyse aggression-based dominance hierarchies formed by worker ants in Diacamma sp. as large directed networks. We show that the observed dominance networks are perfect or approximate directed acyclic graphs, which are consistent with perfect linear hierarchy. The observed networks are also sparse and random but significantly different from networks generated through thinning of the perfect linear tournament (i.e., all individuals are linearly ranked and dominance relationship exists between every pair of individuals). These results pertain to global structure of the networks, which contrasts with the previous studies inspecting frequencies of different types of triads. In addition, the distribution of the out-degree (i.e., number of workers that the focal worker attacks), not in-degree (i.e., number of workers that attack the focal worker), of each observed network is right-skewed. Those having excessively large out-degrees are located near the top, but not the top, of the hierarchy. We also discuss evolutionary implications of the discovered properties of dominance networks.

Abstract:
The development of fibrosis in hepatitis C patients is associated with increased rates of liver cancer. Assessing hepatic fibrosis during interferon treatment for chronic hepatitis C is thus an important factor in treatment planning. Complications such as bleeding may occur in association with liver biopsy and there are also some reports of sampling error [1,2]. In recent years, however, a number of studies looking at noninvasive means of assessing hepatic fibrosis have appeared in the literature [3-5]. The present study was conducted to determine whether it would be possible to apply an easily performed technique of myocardial examination to hepatic fibrosis. We have already documented our findings for strain rate imaging used to differentiate the normal condition, chronic hepatitis and cirrhosis of the liver identified by diagnostic imaging and haematology data [6]. In this study, patients identified by liver biopsy were investigated, and a comparative investigation with several fibrosis markers was carried out.

Aim: We measured carnitine levels in patients with
carnitine including dialysis patients, and examined whether administration of
L-carnitine improved muscle symptoms. Methods: We measured carnitine levels in
27 patients with liver cirrhosis who were receiving treatment in our hospital,
and administered L-carnitine (600 mg - 1800 mg) to patients having muscle
cramps for approximately one month and examined the presence/absence of the
symptom. We measured carnitine concentration before and after dialysis, before
dialysis after the administration to eight dialysis patients, before and after
the administration to 19 nondialytic patients. Results: The total carnitine
levels before the dialysis of dialysis patients were an average of 42.2 μmol/L
and fell to 17.7 μmol/L after more dialysis, but it was increased to 155 μmol/L
after the administration of L-carnitine. In the nondialytic patients, the
total carnitine levels were significantly increased from 71.7 μmol/L to 101.7
μmol/L after the administration of L-carnitine (P = 0.038). For symptomatic patients, significant improvement of
muscle clamps was observed in the L-carnitine administrated group when compared
with the non-administrated group (P =
0.0002). Conclusions: Total carnitine levels were low even before dialysis in
the dialysis patients with liver cirrhosis in particular and they further
decreased after the dialysis. Administration of L-carnitine increased the total
carnitine levels and improved the symptom. Based on these results, we conclude
that L-carnitine is useful for carnitine deficiency in patients with liver
cirrhosis.