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This paper presents an unsupervised method for assembling semantic knowledge from a part-of- speech tagged corpus using graph algorithms. The graph model is built by linking pairs of words which …...

An unsupervised method for word sense disam- biguation using a bilingual comparable corpus was developed. First, it extracts statistically significant pairs of related words from the corpus of each lan- …...

In this paper we show that an unsupervised method for ranking word senses automatically can be used to iden- tify infrequently occurring senses. We demonstrate this using a ranking of …...

We propose a completely unsupervised method for mining parallel sentences from quasi-comparable bilingual texts which have very different sizes, and which include both in-topic and off-topic documents. We discuss and …...

We present an unsupervised method for labelling the arguments of verbs with their semantic roles. Our bootstrapping algorithm makes initial unam- biguous role assignments, and then iteratively up- dates the …...

Spoken language generation for dialogue systems requires a dictionary of mappings between semantic representations of con- cepts the system wants to express and re- alizations of those concepts. Dictionary creation …...

Topic segmentation and identification are of- ten tackled as separate problems whereas they are both part of topic analysis. In this article, we study how topic identification can help to …...

We have participated in the Multilingual Chinese-English Lexical Sample Task of SemEval-2007. Our system disambiguates senses of Chinese words and finds the correct translation in English by using the web …...

We present an automatic method for sense- labeling of text in an unsupervised manner. The method makes use of distributionally similar words to derive an automatically labeled training set, which …...

This paper presents a method for mining potential troubles or obstacles related to the use of a given object. Some exam- ple instances of this relation are ⟨medicine, side effect⟩ …...

We conduct large-scale experiments to in- vestigate optimal features for classification of verbs in biomedical texts. We intro- duce a range of feature sets and associated extraction techniques, and evaluate …...

Keyphrases are widely used as a brief summary of documents. Since man- ual assignment is time-consuming, vari- ous unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. …...

Domain-specific terms provide vital se- mantic information for many natural lan- guage processing (NLP) tasks and appli- cations, but remain a largely untapped re- source in the field. In this …...

This paper presents an unsupervised method for extracting parallel sentence pairs from a comparable corpus. A trans- lation system is used to mine the compa- rable corpus and to detect …...

AbstractDomain specific entity recognition often relies on domain-specific knowledge to improve system performance. However, such knowledge often suffers from limited domain portability and is expensive to build and maintain. Therefore, …...

In this paper we propose a completely un- supervised method for open-domain en- tity extraction and clustering over query logs. The underlying hypothesis is that classes defined by mining search …...

Taxonomies are an important resource for a variety of Natural Language Processing (NLP) applications. Despite this, the current state- of-the-art methods in taxonomy learning have disregarded word polysemy, in effect, …...

Word Sense Disambiguation (WSD) of- ten relies on a context model or vector constructed from the words that co-occur with the target word within the same text windows. In most …...

Metonymic language is a pervasive phe- nomenon. Metonymic type shifting, or ar- gument type coercion, results in a selectional restriction violation where the argument’s se- mantic class differs from the …...

This paper presents an unsupervised method for deriving inference axioms by composing semantic relations. The method is indepen- dent of any particular relation inventory. It relies on describing semantic relations …...

AbstractIn this paper, we describe and evaluate an unsupervised method for acquiring pairs of lexical entries belonging to the same morphological family, i.e., derivationally related words, starting from a purely …...

We present an unsupervised method for in- ducing verb classes from verb uses in giga- word corpora. Our method consists of two clustering steps: verb-specific seman- tic frames are first …...

In this paper, we describe our unsupervised method submitted to the Cross-Level Se- mantic Similarity task in Semeval 2014 that computes semantic similarity between two different sized text fragments. Our …...

Although the distributional hypothesis has been applied successfully in many natural language processing tasks, systems using distributional information have been lim- ited to a single domain because the dis- tribution …...

In this paper, we describe the OPI system participating in the Semeval-2014 task 3 Cross-Level Semantic Similarity. Our ap- proach is knowledge-poor, there is no ex- ploitation of any structured …...

In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books. …...

. Keyphrases are single or multi-word expressions that represent the main content of a document. As keyphrases are useful in many applications such as document indexing or text summarization, and …...

In this paper we present a novel method for unsupervised coreference resolution. We introduce a precision-oriented inference method that scores a candidate entity of a mention based on the most …...

We present an unsupervised method to find lexical variations in Roman Urdu informal text. Our method includes a phonetic algorithm UrduPhone, a feature- based similarity function, and a clustering algorithm …...

AbstractMethods for text simplification using the framework of statistical machine translation have been extensively studied in recent years. However, building the monolingual parallel corpus necessary for training the model requires …...

AbstractIn this paper we report an unsupervised method aimed to identify whether an attribute is discriminative for two words (which are treated as concepts, in our particular case). To this …...

AbstractWe propose a simple unsupervised method for extracting pseudo-parallel monolingual sentence pairs from comparable corpora representative of two different text styles, such as news articles and scientific papers. Our approach …...

AbstractIn cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a …...

AbstractWe propose an unsupervised method for sentence summarization using only language modeling. The approach employs two language models, one that is generic (i.e. pretrained), and the other that is specific …...

AbstractWe focus on the task of Frequently Asked Questions (FAQ) retrieval. A given user query can be matched against the questions and/or the answers in the FAQ. We present a …...

AbstractKB-to-text task aims at generating texts based on the given KB triples. Traditional methods usually map KB triples to sentences via a supervised seq-to-seq model. However, existing annotated datasets are …...

AbstractWhat do linguistic models reveal about the emotions associated with words? In this study, we consider the task of estimating word-level emotion intensity scores for specific emotions, exploring unsupervised, supervised, …...

AbstractWe propose an unsupervised method for the identification of bridge phrases in multi-hop question answering (QA). Our method constructs a graph of noun phrases from the question and the available …...

AbstractKnowledge graph integration typically suffers from the widely existing dangling entities that cannot find alignment cross knowledge graphs (KGs). The dangling entity set is unavailable in most real-world scenarios, and …...

AbstractTo guide the generation of large pretrained language models (LM), previous work has focused on directly fine-tuning the language model or utilizing an attribute discriminator. In this work, we propose …...

AbstractWe propose a new unsupervised method for lexical substitution using pre-trained language models. Compared to previous approaches that use the generative capability of language models to predict substitutes, our method …...

AbstractThis work presents an unsupervised method of selecting filters and threshold values for the OpusFilter parallel corpus cleaning toolbox. The method clusters sentence pairs into noisy and clean categories and …...