Detection of Compound Nouns and Light Verb Constructions using I ndo W ord N et (2016)

Abstract: Detection of MultiWord Expressions (MWEs) is one of the fundamental problems in Natural Language Processing. In this paper, we focus on two categories of MWEs - Compound Nouns and Light Verb Constructions. These two categories can be tackled using knowledge bases, rather than pure statistics. We investigate usability of IndoWordNet for the detection of MWEs. Our IndoWordNet based approach uses semantic and ontological features of words that can be extracted from IndoWordNet. This approach has been tested on Indian languages viz., Assamese, Bengali, Hindi, Konkani, Marathi, Odia and Punjabi. Results show that ontological features are found to be very useful for the detection of light verb constructions, while use of semantic properties for the detection of compound nouns is found to be satisfactory. This approach can be easily adapted by other Indian languages. Detected MWEs can be interpolated into WordNets as they help in representing semantic knowledge.

## introduction multiword expressions or mwes can be described as idiosyncratic interpretations that crosses word boundaries or spaces @xcite . mwe is formed by atleast two words which are syntactically and/or semantically idiosyncratic in nature. for example, swimming pool, telephone booth, strong coffee, pay attention, fast food, etc. are some of the mwes in english, while धन दौलत (dhana daulata, wealth), वादा करना (vaadaa karanaa, to promise), मार डालना (maara daalanaa, to kill), धीरे धीरे (dhiire dhiire, slowly), etc. are some of the mwes in hindi. in past, ample number of approaches have been proposed in literature for the detection of mwes @xcite , @xcite , @xcite , @xcite , @xcite . however, for indian languages, many researchers have proposed statistical and rule based approaches @xcite , @xcite , @xcite , @xcite , @xcite , @xcite , @xcite . this paper focuses on indian languages viz., assamese, bengali, hindi, konkani, marathi, odia and punjabi for the detection of mwes. these languages are part of the indoword-net 1 . to the best of our knowledge, the indowordnet based approach is being used for the first time for detecting mwes. this approach is restricted for two categories of mwes: compound nouns (noun+noun) and light verb constructions @xcite . semantic features of words are used for detecting compound nouns, while ontological features are used for detecting light verb constructions. the motivation behind this work is that, the roadmap of the paper is as follows. section 2 covers the classification of mwes. the indowordnet based approach is explained in section 3. section 4 details the experimental setup. results are presented in section 5 and discussed in section 6. related work is given in section 7, followed by conclusion and future work. ## mwes classification mwes are classified based on their lexical and semantic characteristics @xcite . this has been further studied from indian language perspective and expanded as shown in figure 1 . as we can see in figure 1 , we modified the @xcite classification by adding light verb constructions and its further classification which is needed for indian languages. mwes are classified into two broad categories. they are lexicalized phrases and institutional phrases. the meaning of lexicalized phrases cannot be construed from its individual units that make up the phrase, as they exhibit syntactic and/or semantic idiosyncrasy. on the other hand, the meaning of institutional phrases can be construed from its individual units that make up the phrase. however, they exhibit statistical idiosyncrasy. institutional phrases are not in the scope of this paper. lexicalized phrases are further classified into three sub-classes viz., fixed, semi-fixed and syntactically flexible expressions. in this paper, we focus on compound nouns and light verb constructions which fall under the semi-fixed and syntactically flexible categories respectively. ## indowordnet based approach our indowordnet based approach uses various semantic and ontological features from the indowordnet. the semantic features are used for cn detection while ontological features are used for lvc detection. now, we explain the indowordnet based approach for each of these categories. ## experiments we performed experiments on some indian languages viz., hindi, marathi, bengali, punjabi, konkani, odia, assamese for the detection of compound nouns and conjunct verbs. however, for compound verb detection, we performed experiments only on hindi and marathi due to unavailability of gold data for other languages. the gold data for these experiments is created by automatically extracting noun+noun, noun+verb, adj+verb and verb+verb wordpair combinations. these word-pairs are extracted from the generic domain in-house corpus. out of these word-pairs, 1000 noun+noun word-pairs are detected as cns for each of the seven languages mentioned above, while 399 and 504 verb+verb wordpairs are detected as cpvs for marathi and hindi respectively. @xcite adj+verb word-pairs are detected as cjvs for hindi, marathi, bengali, punjabi, konkani, odia, assamese languages respectively. three lexicographers were engaged in this activity and the inter-annotator agreement is found to be 0.8. ## results in this section, results of the experiments are presented and discussed in detail. ## discussions results of cjv detection for noun+verb and adj+verb combinations are found to be promising. this may be because, our in-dowordnet based approach uses ontological properties of words wherein coverage of nouns and adjectives is high in indowordnet. while, the results of the detection of cpvs are found to be almost 100% for hindi and 88% for marathi. this also used ontological properties of words. hence, we can say that indoword-net based approach is very useful for the detection of cjvs and cpvs. ## related work most of the proposed approaches for the detection of multiword expressions are statistical in nature. they are based on association methods @xcite , deep linguistics based methods @xcite , word embeddings based methods @xcite , etc. the detection of mwes for indian languages is not explored much by researchers due to the reasons such as unavailability of gold data @xcite , unstructured classification of mwes, improper universal theory, etc. in literature, @xcite used random forest approach for compound noun detection for bengali language. @xcite used a classification based approach for extracting noun-verb collocations for hindi language. @xcite used parallel corpus alignment and part-of-speech tag projection to extract complex predicates. however, our indowordnet based approach uses ontological and semantic features of words to detect mwes. the focus is restricted for the detection of compound nouns and light verb constructions. ## conclusion and future work detection of multiword expressions is the fundamental problem and a challenging task in the area of nlp. to address this problem, an indowordnet based approach is proposed in this paper. the focus is restricted to the detection of compound nouns and light verb constructions. semantic features of words from indowordnet are used for the detection of compound nouns, while ontological features of words are used for the detection of light verb constructions. the indowordnet based approach is tested on some indian languages viz., assamese, bengali, hindi, konkani, marathi, odia, punjabi. it has been observed that our approach gives encouraging results for the detection of light verb constructions as compared to compound nouns. in future, the detected mwes can be incorporated in indowordnet as they can help to represent the lexical knowledge. this approach can be used in nlp applications viz., word sense disambiguation, machine translation, information retrieval, question answering, sentiment analysis, etc. it can be implemented and tested for other indian languages.