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Word Similarity

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Language Technology Group University of Hamburg

uhh-lt.github.io/senseasim/

Word Similarity

Task:

§ Compute the similarity or relatedness between two arbitrary words

Common Methodology:

1. use vector representation and of words and

2. compute cosine similarity

Faculty

OF MATH, Computer Science and Natural Sciences

v

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u

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w

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APPROACH

1. Retrofit existing word embeddings:

Compute vector representation of word senses

2. Compute similarity as argmax (we tested 3 options)

arg max

k

cos(ˆ v w k , v u ) arg max

l

cos(v w , ˆ v l u )

arg max

k, l

cos(ˆ v w k , ˆ v l u )

<latexit sha1_base64="BwLJgHT7rImeEovZ0TwaO/ahPpg=">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</latexit><latexit sha1_base64="07XWlKooXUREWIMnRTnO6YGQPI0=">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</latexit><latexit sha1_base64="07XWlKooXUREWIMnRTnO6YGQPI0=">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</latexit><latexit sha1_base64="QEdtabvitCSZoW4iB8hLpw+k06A=">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</latexit>

sim(w, u) =

<latexit sha1_base64="7VXUCKShjRsucIY0lCw91xAUTlg=">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</latexit><latexit sha1_base64="oFEpDyIz0X8sJnihGVW3MGcgKII=">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</latexit><latexit sha1_base64="oFEpDyIz0X8sJnihGVW3MGcgKII=">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</latexit><latexit sha1_base64="OZB4HteZ8VH1p0zMBPmPVSq5UH8=">AAAF0XiclVRfb9MwEA+shVH+bfDIi8U0qYhQtbyANCFN8MITAo2xSfOIHPfamDp2ZF/WlcgI8cq3441PwNfgknWj27oHHEW53N3vd+e7s9NCK4/9/u9r11da7Rs3V291bt+5e+/+2vqDT96WTsKutNq6/VR40MrALirUsF84EHmqYS+dvKnte0fgvLLmI84KOMzF2KiRkgJJlayv/OG2ACfQOiNyqLzKQ3cal0/YK9bZ5BpGyCsSUhgrUwnnxCxUOpBmETZVmGmVK/TkMuZxLo4DP1Ek1SSwb4xL67s8F5ilo4pnAqujED5PkmnMTrVHIaGwnP8Pt77ETSwLnGeR9An3/7FPYr51OcLy7M9FoiBghvNy0Y9T4wx7nc36WUZDxeaamjYUi9WYsqesO3g2t1D6mS+EhOrZAPLAfZmfJVoyvsW4Ms3nXD/RFiHJuzvJ9PPkSbhAsVD4xcx2QlI1MmKlnDUhNNblsyDr/VWMIxxj9VUZGUuhpSrzuJ408LVUWBS+kVhOc+qEjlmv16N0Qtx05BTvEYBsOWDtIm1BW4mZ0CXBajQNuDANzymcncdnymHMiEaQzxcQxhONtpgBCakVbhizDAReRVCUiHVIqcs0ZmMHYGI2tZZgQ6eOatMVQGUmBLCaPFMtJP1MM4UQM+uEGcMZbulo8KbCO80o8JTQXgufsemCtm7uv/40FkaHNanosIZ6fq4+BM3BStY2+r1+s9hlYTAXNqL5ep+s/eJDK8scDErKxh8M+gUeUuKopIbQ4aUHmqWJGMMBifW0+cOquZEC2yTNkI2so9cga7SLiErk3s/ylDzrJP1FW61cZjsocfTysFKGGgVGngQalZqhZfX1xobKgUQ9I0FIpyhXJjPhhKS2+g4VYXBxy5eFT897g35v8KG/sf16Xo7V6FH0OOpGg+hFtB29jd5Hu5FsvWthK7S+tXfas/b39o8T1+vX5piH0bnV/vkXbPP2DQ==</latexit>

Evaluation

§ Sense embeddings with sense induction (AutoExtend, AdaGram)

§ Several word embeddings and retrofitted word embeddings:

SGNS (Word2Vec), Glove, SymPat (symmetric patterns), LSA, ParaGram (from PPDB)

§ Datasets: SimLex999, MEN, SimVerb, WordSim353

Idea

Retrofit Word vectors to Vectors of Senses using Sense Inventories

§ A sense inventory is a collection of synsets for a particular word

§ Where the word itself is not present any of its synsets:

§ Example:

w

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S

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{zinc,calcium,magnesium,potassium, mineral, ...}, { steel, metal, copper, aluminum, titanium, ... } ,

{shirt, steam, jeans, clothes, board, heat, ...}, { putter, club, green, wood, driver, ... } ,

{ pink, gold, black, white, orange, ... } 9 >

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S

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iron Nutrition Atomic Elements Clothes

Golf Sports Color

Issue

Meaning is Lost

§ Standard word embeddings project one word to exactly one vector

§ Different senses of words are neglected

→ Minor senses are underrepresented

→ Major senses are amplified

→ Source corpus / embedding defines the quality of the similarities

Sense Inventory

§ JoBimText * unsupervised sense inventory - via Web API http://jobimtext.org

§ Sense induction by clustering → in the paper!

Figure:

Visualization is based on terms on the unit circle. Inner circle: sense vectors and original word vectors. Outer circles:

Sense inventory terms of the words.

Table:

Spearman correlation scores. Sense-aware similarities are marked with '-S'. Underlined values: the winning system with a slight margin (< 0.03). Bold face values: winning system with a larger margin.

Lower part: evaluates only the noun pair parts of the datasets (indicated by '-N').

sim(w, u) = cos(v w , v u )

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Lrec 2018, Miyazaki, Japan

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2.2 Selection and Sense Classification of Ambiguous Turkish Verbs and Nouns The average number of senses for Turkish words can be significantly high leading to many problems for

We compare the evaluation results obtained for the Sparse and Dense approaches with three baselines: the AdaGram model (AdaGram), the same sense for all the instances per lemma

While the comparison of results to other methods on the same coarse-grained data sets suggests that lexical expansion using a distributional thesaurus leads to more

Cost was kept relatively low using crowdsourcing, while the number of examples per sense is high, specifically targeting supervised ap- proaches that predict

The best results are obtained using relations between terms given by UMLS, a method which achieves 74% precision, 66% coverage for English and 79% preci- sion, 73% coverage for

While the comparison of results to other methods on the same coarse-grained data sets suggests that lexical expansion using a distributional thesaurus leads to more

The similarity-based (SB) baselines, also used as a back-off for the hybrid approaches (+SB), were created using the approach reported in Gurevych et al. Note that for GN, the

For classifying Explicit connectives we follow a feature-based approach, developing features based on word embeddings and semantic similarity mea- sured between parts of the