Chikara Hashimoto

The hinoki treebank a treebank for text understanding

In this paper we describe the motivation for and construction of a new Japanese lexical resource: the Hinoki treebank. The treebank is built from dictionary definition sentences, and uses an HPSG grammar to encode the syntactic and semantic information. We then show how this treebank can be used to extract thesaurus information from definition sentences in a language-neutral way using minimal recursion semantics.

Large-Scale Verb Entailment Acquisition from the Web

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.

Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features

We propose a supervised method of extracting event causalities like conduct slash-and-burn agriculture! exacerbate desertification from the web using semantic relation (between nouns), context, and association features. Experiments show that our method outperforms baselines that are based on state-of-the-art methods. We also propose methods of generating future scenarios like conduct slash-and-burn agriculture! exacerbate desertification! increase Asian dust (from China)! asthma gets worse. Experiments show that we can generate 50,000 scenarios with 68% precision. We also generated a scenario deforestation continues! global warming worsens! sea temperatures rise! vibrio parahaemolyticus fouls (water), which is written in no document in our input web corpus crawled in 2007. But the vibrio risk due to global warming was observed in Baker-Austin et al. (2013). Thus, we “predicted” the future event sequence in a sense.

Packaging and transduction of non-T3 DNA by bacteriophage T3.

A defined in vitro system for packaging T3 DNA also packaged other linear DNAs, including T4, lambda, and plasmid DNAs. The packaging capacity was determined to be 40 kb (kilobase pairs) by measuring the packaged length of T4 DNA. Packaged lambda and plasmid DNAs were injected into host cells to form plaques and transductants, respectively. The yield of transducers increased by using artificially ligated plasmid oligomers. The T3 mutant in gene 3 endonuclease (T3 3-) packaged plasmid DNA during abortive infection and transduced it into the recipient. Transduction of recombinant plasmids was not affected by the presence of the terminally redundant sequence (TR sequence) but increased by 4 orders of magnitudes when the genetic right-end 2.7-kb sequences, containing gene 19 (E1) but lacking TR, were present and by 7 orders when both E1 and TR sequences were present. However, these sequences did not increase transduction of these plasmids by T7 3-. Analysis of the structure of transduced plasmid DNAs indicates that transducing particles carry head-to-tail oligomers of plasmid DNA with the same termini as those of T3 genomic DNA. The mechanism of formation of transducing particles is discussed.

Construction of an Idiom Corpus and its Application to Idiom Identification based on WSD Incorporating Idiom-Specific Features

Some phrases can be interpreted either idiomatically (figuratively) or literally in context, and the precise identification of idioms is indispensable for full-fledged natural language processing (NLP). To this end, we have constructed an idiom corpus for Japanese. This paper reports on the corpus and the results of an idiom identification experiment using the corpus. The corpus targets 146 ambiguous idioms, and consists of 102, 846 sentences, each of which is annotated with a literal/idiom label. For idiom identification, we targeted 90 out of the 146 idioms and adopted a word sense disambiguation (WSD) method using both common WSD features and idiom-specific features. The corpus and the experiment are the largest of their kind, as far as we know. As a result, we found that a standard supervised WSD method works well for the idiom identification and achieved an accuracy of 89.25% and 88.86% with/without idiom-specific features and that the most effective idiom-specific feature is the one involving the adjacency of idiom constituents.

Compilation of an idiom example database for supervised idiom identification

Some phrases can be interpreted in their context either idiomatically (figuratively) or literally. The precise identification of idioms is essential in order to achieve full-fledged natural language processing. Because of this, the authors of this paper have created an idiom corpus for Japanese. This paper reports on the corpus itself and the results of an idiom identification experiment conducted using the corpus. The corpus targeted 146 ambiguous idioms, and consists of 102,856 examples, each of which is annotated with a literal/idiomatic label. All sentences were collected from the World Wide Web. For idiom identification, 90 out of the 146 idioms were targeted and a word sense disambiguation (WSD) method was adopted using both common WSD features and idiom-specific features. The corpus and the experiment are both, as far as can be determined, the largest of their kinds. It was discovered that a standard supervised WSD method works well for idiom identification and it achieved accuracy levels of 89.25 and 88.86%, with and without idiom-specific features, respectively. It was also found that the most effective idiom-specific feature is the one that involves the adjacency of idiom constituents.

Detecting Japanese idioms with a linguistically rich dictionary

Detecting idioms in a sentence is important to sentence understanding. This paper discusses the linguistic knowledge for idiom detection. The challenges are that idioms can be ambiguous between literal and idiomatic meanings, and that they can be “transformed” when expressed in a sentence. However, there has been little research on Japanese idiom detection with its ambiguity and transformations taken into account. We propose a set of linguistic knowledge for idiom detection that is implemented in an idiom dictionary. We evaluated the linguistic knowledge by measuring the performance of an idiom detector that exploits the dictionary. As a result, more than 90% of the idioms are detected with 90% accuracy.

Generating information-rich taxonomy from Wikipedia

Even though hyponymy relation acquisition has been extensively studied, “how informative such acquired hyponymy relations are” has not been sufficiently discussed. We found that the hypernyms in automatically acquired hyponymy relations were often too vague or ambiguous to specify the meaning of their hyponyms. For instance, hypernym work is vague and ambiguous in hyponymy relations work/Avatar and work/The Catcher in the Rye. In this paper, we propose a simple method of generating intermediate concepts of hyponymy relations that can make such (vague) hypernyms more specific. Our method generates such an information-rich hyponymy relation as work / work by film director / work by James Cameron / Avatar from the less informative relation work/Avatar. Furthermore, the generated relation work by film director/Avatar can be paraphrased into a new relation movie/Avatar. Experiments showed that our method successfully acquired 2,719,441 enriched hyponymy relations with one intermediate concept with 0.853 precision and another 6,347,472 hyponymy relations with 0.786 precision.

Blog Categorization Exploiting Domain Dictionary and Dynamically Estimated Domains of Unknown Words

This paper presents an approach to text categorization that i) uses no machine learning and ii) reacts on-the-fly to unknown words. These features are important for categorizing Blog articles, which are updated on a daily basis and filled with newly coined words. We categorize 600 Blog articles into 12 domains. As a result, our categorization method achieved an accuracy of 94.0% (564/600).

Intra-sentential Zero Anaphora Resolution using Subject Sharing Recognition

In this work, we improve the performance of intra-sentential zero anaphora resolution in Japanese using a novel method of recognizing subject sharing relations. In Japanese, a large portion of intrasentential zero anaphora can be regarded as subject sharing relations between predicates, that is, the subject of some predicate is also the unrealized subject of other predicates. We develop an accurate recognizer of subject sharing relations for pairs of predicates in a single sentence, and then construct a subject shared predicate network, which is a set of predicates that are linked by the subject sharing relations recognized by our recognizer. We finally combine our zero anaphora resolution method exploiting the subject shared predicate network and a state-ofthe-art ILP-based zero anaphora resolution method. Our combined method achieved a significant improvement over the the ILPbased method alone on intra-sentential zero anaphora resolution in Japanese. To the best of our knowledge, this is the first work to explicitly use an independent subject sharing recognizer in zero anaphora resolution.