In this paper, we present Espresso, a weakly-supervised, general-purpose, and accurate algorithm for harvesting semantic relations. The main contributions are: i) a method for exploiting generic patterns by filtering incorrect instances using the Web; and ii) a principled measure of pattern and instance reliability enabling the filtering algorithm. We present an empirical comparison of Espresso with various state of the art systems, on different size and genre corpora, on extracting various general and specific relations. Experimental results show that our exploitation of generic patterns substantially increases system recall with small effect on overall precision. 1

Most work on unsupervised entailment rule acquisition focused on rules between templates with two variables, ignoring unary rules- entailment rules between templates with a single variable. In this paper we investigate two approaches for unsupervised learning of such rules and compare the proposed methods with a binary rule learning method. The results show that the learned unary rule-sets outperform the binary rule-set. In addition, a novel directional similarity measure for learning entailment, termed Balanced-Inclusion, is the best performing measure. 1

In this paper, we present Espresso, a weakly-supervised iterative algorithm combined with a web-based knowledge expansion technique, for extracting binary semantic relations. Given a small set of seed instances for a particular relation, the system learns lexical patterns, applies them to extract new instances, and then uses the Web to filter and expand the instances. Preliminary experiments show that Espresso extracts highly precise lists of a wide variety of semantic relations when compared with two state of the art systems. 1.

This paper investigates an isolated setting of the lexical substitution task of replacing words with their synonyms. In particular, we examine this problem in the setting of subtitle generation and evaluate state of the art scoring methods that predict the validity of a given substitution. The paper evaluates two context independent models and two contextual models. The major findings suggest that distributional similarity provides a useful complementary estimate for the likelihood that two Wordnet synonyms are indeed substitutable, while proper modeling of contextual constraints is still a challenging task for future research. 1

Vector space models of word meaning typically represent the meaning of a word as a vector computed by summing over all its corpus occurrences. Words close to this point in space can be assumed to be similar to it in meaning. But how far around this point does the region of similar meaning extend? In this paper we discuss two models that represent word meaning as regions in vector space. Both representations can be computed from traditional point representations in vector space. We find that both models perform at over 95 % F-score on a token classification task.

We present a vector space model that supports the computation of appropriate vector representations for words in context, and apply it to a paraphrase ranking task. An evaluation on the SemEval 2007 lexical substitution task data shows promising results: the model significantly outperforms a current state of the art model, and our treatment of context is effective. 1

At least two kinds of relations exist among related words: taxonomical relations and thematic relations. Both relations identify related words useful to language understanding and generation, information retrieval, and so on. However, although words with taxonomical relations are easy to identify from linguistic resources such as dictionaries and thesauri, words with thematic relations are difficult to identify because they are rarely maintained in linguistic resources. In this paper, we sought to extract thematically (non-taxonomically) related word sets among words in documents by employing case-marking particles derived from syntactic analysis. We then verified the usefulness of word sets with non-taxonomical relation that seems to be a thematic relation for information retrieval. 1.

Textual entailment recognition plays a fundamental role in tasks that require indepth natural language understanding. In order to use entailment recognition technologies for real-world applications, a large-scale entailment knowledge base is indispensable. This paper proposes a conditional probability based directional similarity measure to acquire verb entailment pairs on a large scale. We targeted 52,562 verb types that were derived from 108 Japanese Web documents, without regard for whether they were used in daily life or only in specific fields. In an evaluation of the top 20,000 verb entailment pairs acquired by previous methods and ours, we found that our similarity measure outperformed the previous ones. Our method also worked well for the top 100,000 results. 1

We introduce a mechanism that provides key words which can make human-computer interaction increase in the course of information retrieval, by using natural language processing technology and mathematic measure for calculating degree of inclusion. We show what type of words should be added to the current query, i.e. keywords which previously had been input, in order to make humancomputer interaction more creative. We try to extract related word sets from documents by employing casemarking particles derived from syntactic analysis. Then, we verify which kind of related words is more useful as an additional word for retrieval support.

The study addresses the problem of automatic acquisition of entailment relations between verbs. While this task has much in common with paraphrases acquisition which aims to discover semantic equivalence between verbs, the main challenge of entailment acquisition is to capture asymmetric, or directional, relations. Motivated by the intuition that it often underlies the local structure of coherent text, we develop a method that discovers verb entailment using evidence about discourse relations between clauses available in a parsed corpus. In comparison with earlier work, the proposed method covers a much wider range of verb entailment types and learns the mapping between verbs with highly varied argument structures. 1